JP7774966B2 - Analysis method and analysis device - Google Patents

Description

The present invention relates to an analytical method and apparatus for analyzing cells in a specimen.

Patent Document 1 describes a method for classifying white blood cells into subpopulations using fluorescence and light scattering parameters obtained by flowing blood cells through a flow cell. This method classifies white blood cell subpopulations based on multiple parameters, including axial light loss (ALL), intermediate angle forward scatter (IAS), fluorescence (FL1), and polarized side scatter (PSS), which are generated by irradiating light onto blood cells flowing through a flow cell. The classified white blood cell subpopulations are displayed on a scatter plot.

Special Publication No. 2016-514267

Conventional cell classification methods, such as that disclosed in Patent Document 1, uniformly classify each cell into one category. However, even the same neutrophils can have different morphological characteristics depending on their level of maturity, and some neutrophils may have morphological characteristics similar to immature granulocytes. With conventional cell classification methods, even if cells with characteristics of normal cells such as neutrophils and abnormal cells such as immature granulocytes are included, such cells are uniformly classified into one type, which may not lead to more detailed analysis.

The present invention aims to provide a new analytical method and analytical device that enables more detailed analysis of cells that have the characteristics of multiple cell types.

The analytical method of the present invention is an analytical method for analyzing a specimen containing cells, which comprises irradiating a measurement sample prepared from the specimen with light to detect light generated from the cells, obtaining characteristic data of each of a plurality of cells contained in the specimen based on the detected light, analyzing the characteristic data using a deep learning algorithm to determine, for each of the cells, the probability that each of the cells corresponds to one of a plurality of cell types and determining a first cell type and a second cell type based on the probability, and generating a first analytical result based on the first cell type by counting and/or classifying the plurality of cells contained in the specimen, and generating a second analytical result based on the second cell type .

The analytical device of the present invention is an analytical device for analyzing a specimen containing cells, and includes a specimen preparation unit that prepares a measurement specimen from the specimen, a detection unit that irradiates the measurement specimen prepared by the specimen preparation unit with light and detects light emitted from the cells, a signal processing unit that acquires cell feature data for each of a plurality of cells contained in the specimen based on the detected light, a display unit , and an information processing unit having at least one processor . The processor analyzes the feature data using a deep learning algorithm to determine, for each of the cells, a probability that the cell corresponds to one of a plurality of cell types and a first cell type and a second cell type based on the probability, and generates a first analysis result based on the first cell type and a second analysis result based on the second cell type by counting and/or classifying the plurality of cells contained in the specimen.

The present invention enables more detailed analysis of cells that have characteristics of multiple cell types.

FIG. 1 is a diagram showing a cell analyzer and a host computer. FIG. 2 is a block diagram of the measurement unit. FIG. 3 is a block diagram of the sample preparation unit. FIG. 4 is a schematic diagram of the FCM detection unit. FIG. 5 is a diagram illustrating the process of generating a digital signal by the A/D conversion unit. FIG. 6 is a diagram illustrating the structure of the feature parameter data. FIG. 7 is a schematic diagram illustrating the process of cell classification based on a deep learning algorithm. FIG. 8 shows the types of cells analyzed by the deep learning algorithm. FIG. 9 is a block diagram of the processing unit. FIG. 10 is a flowchart illustrating the operation of the cell analyzer. FIG. 11 is a flowchart illustrating the inspection result data generation process. FIG. 12 is a diagram schematically illustrating a first example of the data structure of the test result data. FIG. 13 is a diagram schematically illustrating a second example of the data structure of the test result data. FIG. 14 is a diagram schematically illustrating a third example of the data structure of the test result data. FIG. 15 is a diagram schematically illustrating a fourth example of the data structure of the test result data. FIG. 16 is a diagram schematically illustrating a fifth example of the data structure of the test result data. FIG. 17 is a flowchart of a first example of the test result display process. FIG. 18 is a diagram showing an example of the sample list screen. FIG. 19 is a diagram showing an example of the details screen. FIG. 20 is a diagram showing an example of the analysis screen. FIG. 21 is a diagram showing an example of the analysis screen. FIG. 22 is a diagram showing an example of the analysis screen. FIG. 23 is a diagram showing an example of the analysis screen. FIG. 24 is a flowchart of a second example of the test result display process. FIG. 25 is a diagram showing an example of the analysis screen. FIG. 26 is a diagram showing an example of the analysis screen. FIG. 27 is a diagram showing an example of the analysis screen. FIG. 28 is a diagram showing an example of the analysis screen. FIG. 29 is a flowchart of a third example of the test result display process. FIG. 30 is a flowchart of a fourth example of the test result display process. FIG. 31 is a diagram showing an example of the analysis screen. FIG. 32 is a diagram showing an example of the analysis screen. FIG. 33 is a diagram showing an example of the analysis screen. FIG. 34 is a diagram showing an example of the analysis screen. FIG. 35 is a diagram showing an example of the analysis screen. FIG. 36 is a diagram showing an example of the analysis screen. FIG. 37 is a diagram showing an example of the analysis screen. FIG. 38 is a diagram showing an example of the analysis screen. 39(a) and 39(b) are diagrams showing other examples of scattergrams. FIG. 40 is a flowchart of a fifth example of the test result display process. FIG. 41 is a diagram showing an example of the analysis screen. FIG. 42 is a diagram showing an example of the analysis screen. FIG. 43 is a flow chart of the transmission process to the host computer. 44(a) and 44(b) are diagrams showing examples of the structure of output data. 45(a) and 45(b) are diagrams showing examples of the structure of output data. 46(a) and 46(b) are diagrams showing examples of screens for accepting settings related to output data. FIG. 47 is a diagram showing an example of the configuration of a parallel processing processor. FIG. 48 is a diagram showing an example of mounting a parallel processing processor on a measurement unit. FIG. 49 is a diagram showing an example of mounting a parallel processing processor on a measurement unit. FIG. 50 is a diagram showing an example of mounting a parallel processing processor on a measurement unit. FIG. 51 is a diagram showing an example of mounting a parallel processing processor on a measurement unit. FIG. 52 is a diagram showing an overview of the arithmetic processing executed by the processor and the parallel processing processor. 53(a) and 53(b) are diagrams each showing an outline of a matrix operation executed by a parallel processor. FIG. 54 is a diagram showing that a plurality of arithmetic processes are executed by a parallel arithmetic processor. 55(a) and 55(b) are diagrams each showing an outline of the computational processing related to the convolutional layer. FIG. 56 is a flowchart of cell classification using a deep learning algorithm with a processor and a parallel processing processor. FIG. 57 is a flowchart of the parallel processing execution. FIG. 58 is a block diagram showing another example of the configuration of the cell analyzer. FIG. 59 is a block diagram showing another example of the configuration of the cell analyzer. FIG. 60 is a schematic diagram showing the control of a parallel processing processor by a processor in another example of the configuration of a cell analyzer. FIG. 61 is a block diagram showing another example of the configuration of the cell analyzer. FIG. 62 is a block diagram showing another example of the configuration of the cell analyzer. FIG. 63 is a block diagram showing another example of the configuration of the cell analyzer. FIG. 64 is a block diagram showing another example of the configuration of the cell analyzer. FIG. 65 is a block diagram showing another example of the configuration of the cell analyzer. FIG. 66 is a schematic diagram showing the control of a parallel processing processor by a processor in another example of the configuration of a cell analyzer. FIG. 67 is a block diagram showing another example of the configuration of the cell analyzer. FIG. 68 is a block diagram showing another example of the configuration of the cell analyzer. FIG. 69 is a block diagram showing another example of the configuration of the cell analyzer. 70(a) to 70(c) are diagrams each explaining the structure of a neural network. FIG. 71 is a diagram illustrating the training of a neural network.

The following provides a detailed description of the present invention and its embodiments, with reference to the accompanying drawings. Note that in the following description and drawings, the same reference numerals will denote the same or similar components, and descriptions of the same or similar components will be omitted.

The analytical method described below involves irradiating a measurement sample prepared from a specimen containing cells with light, detecting the light emitted from the cells, obtaining characteristic data for each of the multiple cells contained in the specimen based on the detected light, and analyzing the characteristic data using an artificial intelligence algorithm to classify each cell into multiple cell types.

The specimen may be a biological sample collected from a subject. For example, the biological sample may include peripheral blood such as venous blood and arterial blood, urine, and body fluids other than blood and urine. Body fluids other than blood and urine may include bone marrow fluid, ascites, pleural effusion, cerebrospinal fluid, etc. Hereinafter, body fluids other than blood and urine may be simply referred to as "body fluids." A blood sample is one that allows cell counting and cell type determination, i.e., one that contains cells, and is preferably whole blood. The blood is preferably peripheral blood. For example, the blood may be peripheral blood collected using an anticoagulant such as ethylenediaminetetraacetic acid (sodium salt or potassium salt) or heparin sodium. Peripheral blood may be collected from an artery or a vein.

The cell types classified by this analysis method are based on cell types morphologically classified and vary depending on the type of biological sample. When the biological sample is blood and the blood is collected from a healthy individual, the cell types to be determined in this embodiment include, for example, nucleated cells such as red blood cells and white blood cells, and platelets. Nucleated cells include, for example, neutrophils, lymphocytes, monocytes, eosinophils, and basophils. Neutrophils include, for example, segmented neutrophils and band-shaped neutrophils. When the blood is collected from an unhealthy individual, the nucleated cells include abnormal cells.

Abnormal cells refer to cells that are not normally found in the peripheral blood of healthy individuals. Abnormal cells may include abnormal lymphoid cells. Abnormal lymphoid cells include, for example, atypical lymphocytes (reactive lymphocytes), abnormal lymphocytes including mature lymphomas, and plasma cells. Abnormal cells may include blasts. Blasts include myeloblasts, lymphoblasts, proerythroblasts, basophilic erythroblasts, polychromatic erythroblasts, normochromatic erythroblasts, promegaloblasts, basophilic megaloblasts, polychromatic megaloblasts, and normochromatic megaloblasts. Abnormal cells may include megakaryocytes. Abnormal cells may include immature granulocytes. Immature granulocytes include, for example, promyelocytes, myelocytes, and metamyelocytes.

Abnormal cells may include other abnormal cells not found in the peripheral blood of healthy individuals. Examples of abnormal cells are cells that appear when a patient is afflicted with a disease, such as tumor cells. In the case of the hematopoietic system, examples of diseases include leukemias such as myelodysplastic syndrome, acute myeloblastic leukemia, acute myeloblastic leukemia, acute promyelocytic leukemia, acute myelomonocytic leukemia, acute monocytic leukemia, erythroleukemia, acute megakaryoblastic leukemia, acute myeloid leukemia, acute lymphocytic leukemia, lymphoblastic leukemia, chronic myelogenous leukemia, and chronic lymphocytic leukemia; malignant lymphomas such as Hodgkin's lymphoma and non-Hodgkin's lymphoma; and multiple myeloma.

When the biological sample is urine, cell types may include, for example, red blood cells, white blood cells, epithelial cells such as transitional epithelium and squamous epithelium, etc. Abnormal cells may include, for example, bacteria, fungi such as filamentous fungi and yeast, tumor cells, etc.

When the biological sample is a body fluid that does not normally contain blood components, such as ascites, pleural effusion, or cerebrospinal fluid, the cell types may include, for example, red blood cells, white blood cells, and large cells. "Large cells" here refer to cells that are larger than white blood cells and that have detached from the lining of a body cavity or the peritoneum of an internal organ, such as mesothelial cells, histiocytes, and tumor cells.

When the biological sample is bone marrow fluid, the cell types may include, as normal cells, mature blood cells and immature blood cells. Mature blood cells include, for example, nucleated cells such as red blood cells and white blood cells, and platelets. Nucleated cells such as white blood cells include, for example, neutrophils, lymphocytes, plasma cells, monocytes, eosinophils, and basophils. Neutrophils include, for example, segmented neutrophils and band-shaped neutrophils. Immature blood cells include, for example, hematopoietic stem cells, immature granulocytic cells, immature lymphocytic cells, immature monocytic cells, immature erythroid cells, megakaryocytic cells, and mesenchymal cells. Immature granulocytes may include, for example, metamyelocytes, myelocytes, promyelocytes, and myeloblasts. Immature lymphocytic cells include, for example, lymphoblasts. Immature monocytic cells include, for example, monoblasts. Immature erythroid cells include nucleated red blood cells such as proerythroblasts, basophilic erythroblasts, polychromatic erythroblasts, normochromatic erythroblasts, promegaloblasts, basophilic megaloblasts, polychromatic megaloblasts, and normochromatic megaloblasts. Megakaryocytic cells include, for example, megakaryoblasts.

Abnormal cells that may be contained in bone marrow include, for example, leukemias such as the aforementioned myelodysplastic syndrome, acute myeloblastic leukemia, acute myeloblastic leukemia, acute promyelocytic leukemia, acute myelomonocytic leukemia, acute monocytic leukemia, erythroleukemia, acute megakaryoblastic leukemia, acute myeloid leukemia, acute lymphocytic leukemia, lymphoblastic leukemia, chronic myeloid leukemia, and chronic lymphocytic leukemia; hematopoietic tumor cells of malignant lymphomas such as Hodgkin's lymphoma and non-Hodgkin's lymphoma; and metastatic tumor cells of malignant tumors that have developed in organs other than the bone marrow.

This analysis method is preferably carried out using a cell analyzer that analyzes a specimen containing cells. The cell analyzer may include a sample preparation unit that prepares a measurement specimen from the specimen; a detection unit that irradiates the measurement specimen prepared by the sample preparation unit with light and detects light emitted from the cells; a signal processing unit that acquires cell characteristic data for each of a plurality of cells contained in the specimen based on the detected light; and a control unit that analyzes the characteristic data using an artificial intelligence algorithm to classify each cell into a plurality of cell types. An example of such a cell analyzer is described below.

[1. Basic configuration]
The basic configuration of a cell analyzer will be described with reference to Fig. 1. Fig. 1 is a schematic diagram showing the exterior of a cell analyzer 100. The cell analyzer 100 is an apparatus that analyzes biological samples in accordance with a test order transmitted from a host computer 500. The cell analyzer 100 includes a measurement unit 400 and a processing unit 300. The host computer 500 and the cell analyzer 100 are collectively referred to as a test system 1000.

[2. Configuration of the measurement unit]
The configuration of the measurement unit 400 will be described with reference to Fig. 2. Fig. 2 shows an example of a block diagram of the measurement unit 400. As shown in Fig. 2, the measurement unit 400 includes a specimen aspirating section 450 that aspirates a specimen, a specimen preparing section 440 that prepares a measurement specimen from the aspirated specimen, an FCM detecting section 410 that detects blood cells in the measurement specimen, an analog processing section 420 that processes analog signals output from the FCM detecting section 410, a measurement unit control section 480 that converts signals processed by the analog processing section 420 into digital signals for analysis, and an apparatus mechanism section 430.

Figure 3 is a schematic diagram illustrating the specimen aspirating unit 450 and the sample preparing unit 440. The specimen aspirating unit 450 includes a nozzle 451 for aspirating a blood specimen from a blood collection tube T, and a pump 452 for applying negative/positive pressure to the nozzle 451. The nozzle 451 is inserted into the blood collection tube T by being moved up and down by the device mechanism unit 430. When the pump 452 applies negative pressure while the nozzle 451 is inserted into the blood collection tube T, the blood specimen is aspirated through the nozzle 451.

The sample preparation unit 440 has five reaction chambers 440a-440e. The reaction chambers 440a-440e are used in the DIFF, RET, WPC, PLT-F, and WNR measurement channels, respectively. A hemolytic agent container containing a hemolytic agent and a staining solution container containing a staining solution are connected to each reaction chamber via a flow path. A measurement channel is made up of one reaction chamber and the reagents (hemolytic agent and staining solution) connected to it. For example, the DIFF measurement channel is made up of DIFF hemolytic agent and DIFF staining solution, which are DIFF measurement reagents, and reaction chamber 440a. The other measurement channels are configured similarly. Note that, while the example shown here shows a configuration in which one measurement channel contains one hemolytic agent and one staining solution, one measurement channel does not necessarily have to contain both a hemolytic agent and a staining solution; one reagent may be shared by multiple measurement channels.

After aspirating the blood sample, the nozzle 451 accesses from above one of the reaction chambers 440a-440e that corresponds to the measurement channel corresponding to the order through horizontal and vertical movement by the device mechanism unit 430, and dispenses the aspirated blood sample. The sample preparation unit 440 supplies the corresponding hemolytic agent and staining solution to the reaction chamber into which the blood sample has been dispensed, and prepares the measurement sample by mixing the blood sample, hemolytic agent, and staining solution within the reaction chamber. The prepared measurement sample is supplied from the reaction chamber via the flow path to the FCM detection unit 410, where cells are measured by flow cytometry.

Figure 4 shows an example of the optical system configuration of the FCM detection unit 410. As shown in Figure 4, in measurements using a flow cytometer, when cells contained in a measurement sample pass through a flow cell (sheath flow cell) 4113 provided within the flow cytometer, a light source 4111 irradiates the flow cell 4113 with light, and the scattered light and fluorescent light emitted from the cells in the flow cell 4113 by this light are detected by light-receiving elements 4116, 4121, and 4122.

In Figure 4, light emitted from a laser diode, which is a light source 4111, is irradiated onto cells passing through a flow cell 4113 via an illumination lens system 4112.

As shown in FIG. 4, forward scattered light emitted from particles passing through the flow cell 4113 is received by the light-receiving element 4116 via the collecting lens 4114 and the pinhole portion 4115. The light-receiving element 4116 is, for example, a photodiode. Side scattered light is received by the light-receiving element 4121 via the collecting lens 4117, the dichroic mirror 4118, the bandpass filter 4119, and the pinhole portion 4120. The light-receiving element 4121 is, for example, a photomultiplier tube. The side fluorescent light is received by the light-receiving element 4122 via the collecting lens 4117 and the dichroic mirror 4118. The light-receiving element 4122 is, for example, a photomultiplier tube. Note that an avalanche photodiode or a photomultiplier tube may be used as the light-receiving element 4116 instead of a photodiode. Photodiodes or avalanche photodiodes may be used as the light receiving elements 4121 and 4122.

The light receiving signals output from each light receiving element 4116, 4121, and 4122 are input to the analog processing unit 420 via amplifiers 4151, 4152, and 4153, respectively. The analog processing unit 420 is connected to the A/D conversion unit 482 of the measurement unit control unit 480 (described later) via the signal transmission path 421.

Returning to Figure 2, the analog processing unit 420 performs processes such as noise removal and smoothing on the analog signal input from the FCM detection unit 410, and outputs the processed analog signal to the measurement unit control unit 480.

The measurement unit control unit 480 includes an A/D conversion unit 482, a processor 4831, RAM 4834, a memory unit 4835, a bus controller 4850, a parallel processing processor 4833, a bus 485, and an interface unit 489 that connects to the processing unit 300. The measurement unit control unit 480 includes an interface unit 484 that is interposed between the bus 485 and the A/D conversion unit 482. The measurement unit control unit 480 further includes an interface unit 488 that is interposed between various hardware (i.e., the specimen aspiration unit 450, the device mechanism unit 430, the sample preparation unit 440, and the FCM detection unit 410) and the bus 485.

The A/D conversion unit 482 samples the analog signal output from the analog processing unit 420 at a predetermined sampling rate (e.g., 1024 sampling points at 10 nanosecond intervals, 128 sampling points at 80 nanosecond intervals, or 64 sampling points at 160 nanosecond intervals) and converts it into a digital signal. The A/D conversion unit 482 converts the analog signal from the start of sample measurement to the end of measurement into a digital signal. When multiple types of analog signals (e.g., analog signals corresponding to forward scattered light intensity, side scattered light intensity, and fluorescence intensity) are generated by measurement in a certain measurement channel, the A/D conversion unit 482 converts each analog signal from the start of measurement to the end of measurement into a digital signal. As described with reference to FIG. 4, three types of analog signals (i.e., forward scattered light signal, side scattered light signal, and fluorescence signal) are input to the A/D conversion unit 482 via multiple corresponding signal transmission paths 421. The A/D conversion unit 482 converts each of the analog signals input from the multiple signal transmission paths 421 into a digital signal.

Figure 5 is a schematic diagram for explaining the process of sampling analog signals by the A/D conversion unit 482. When a measurement sample containing cells C is flowed through the flow cell 4113 and light is irradiated onto the flow cell 4113, forward scattered light is generated in the forward direction of the light. Similarly, side scattered light and side fluorescent light are generated to the side of the light. The forward scattered light is received by the light receiving element 4116, and a signal corresponding to the amount of received light is output. The side scattered light is received by the light receiving element 4121, and a signal corresponding to the amount of received light is output. The side fluorescent light is received by the light receiving element 4122, and a signal corresponding to the amount of received light is output. As multiple cells contained in the measurement sample pass through the flow cell 4113, analog signals representing changes in the signal over time are output from the light receiving elements 4116, 4121, and 4122. An analog signal corresponding to forward scattered light is called a "forward scattered light signal," an analog signal corresponding to side scattered light is called a "side scattered light signal," and an analog signal corresponding to side fluorescence is called a "fluorescence signal." One pulse of each analog signal corresponds to one cell.

The analog signals are input to the A/D converter 482. The A/D converter 482 samples the forward scattered light signal, side scattered light signal, and fluorescent light signal from the analog signals input from the light-receiving elements 4116, 4121, and 4122, starting from the point when the level of the forward scattered light signal reaches a level set as a predetermined threshold. The A/D converter 482 samples each analog signal at a predetermined sampling rate (e.g., 1024 sampling points at 10 nanosecond intervals, 128 sampling points at 80 nanosecond intervals, or 64 sampling points at 160 nanosecond intervals). The sampling time is fixed regardless of the pulse size. The sampling time is set to be longer than the time it takes for the analog signal level to rise and fall when a single cell passes through the beam spot of the flow cell 4113. This results in matrix data, whose elements indicate the analog signal levels at multiple points in time, as a digital signal corresponding to a single cell. In this way, the A/D conversion unit 482 generates a digital signal of forward scattered light, a digital signal of side scattered light, and a digital signal of side fluorescent light corresponding to one cell. The A/D conversion is repeated until a predetermined number of cells have been obtained as digital signals, or until a predetermined time has passed since the measurement sample began to flow through the flow cell 4113. As a result, as shown in Figure 5, digital signals are obtained for N cells in the measurement sample by digitizing the waveforms of the analog signals of each cell. In this specification, the collection of sampling data for each cell contained in the digital signal (in the example of Figure 5, a collection of 1024 digital values from t = 0 ns to t = 10240 ns) is referred to as waveform data.

Each piece of waveform data generated by the A/D conversion unit 482 is assigned an index to identify each cell. For example, the index is an integer between 1 and N assigned in the order of the generated waveform data, and the same index is assigned to waveform data of forward scattered light, waveform data of side scattered light, and waveform data of side fluorescent light obtained from the same cell. The index corresponds to the cell ID included in the cell data structure described below.

Since one piece of waveform data corresponds to one cell, the index corresponds to the measured cell. By assigning the same index to waveform data corresponding to the same cell, the deep learning algorithm described below can analyze the forward-scattered light waveform data, side-scattered light waveform data, and fluorescent light waveform data corresponding to each cell and classify the cell type.

In addition to generating waveform data corresponding to each cell, the A/D conversion unit 482 calculates the peak value of the pulse of each signal to generate feature parameter data. FIG. 6 is a schematic diagram showing an example of feature parameter data. In parallel with the process of converting the analog signal of each cell into a digital value to generate waveform data, the A/D conversion unit 482 stores the maximum digital value contained in the waveform data of each cell, starting from the first column of the feature parameter data. Column N stores the value of the cell for which waveform data was generated Nth. The column position corresponds to the index assigned to the beginning of the waveform data. In other words, the digital value corresponding to the waveform data with index "N" is stored in column N. The maximum digital value corresponds to the peak height of the pulse of the analog signal of the cell. Therefore, by extracting the maximum digital value contained in the waveform data of each cell for the forward scattered light signal, side scattered light signal, and side fluorescent light signal, the peak value of the pulse of the forward scattered light signal (referred to as FSCP), the peak value of the pulse of the side scattered light signal (referred to as SSCP), and the peak value of the pulse of the side fluorescent light signal (referred to as SFLP) can be obtained for each cell.

Returning to Figure 2, the A/D conversion unit 482 inputs the generated digital signal and feature parameter data to the bus 485. The bus controller 4850 transmits the digital signal output from the A/D conversion unit 482 to the RAM 4834, for example, by DMA (Direct Memory Access) transfer. The RAM 4834 stores the digital signal and feature parameter data.

The processor 4831 is connected to the interface unit 489, interface unit 488, interface unit 484, RAM 4834, and memory unit 4835 via the bus 485. The processor 4831 is connected to the parallel processing processor 4833 via the bus 485. The processing unit 300 is connected to each component of the measurement unit 400 via the interface unit 489 and the bus 485. The bus 485 is, for example, a transmission path with a data transfer speed of several hundred MB/s or more. The bus 485 may also be configured with a transmission path with a data transfer speed of 1 GB/s or more. The bus 485 transfers data based on, for example, the PCI-Express or PCI-X standard.

The processor 4831 analyzes the digital signal by executing analysis software 4835a stored in the memory unit 4835 using the parallel processing processor 4833.

The processor 4831 is, for example, a CPU (Central Processing Unit). The parallel processing processor 4833 executes multiple arithmetic processes in parallel, which are at least part of the processing related to the analysis of waveform data. The parallel processing processor 4833 is, for example, a GPU (Graphics Processing Unit), FPGA (Field Programmable Gate Array), or ASIC (Application Specific Integrated Circuit). If the parallel processing processor 4833 is an FPGA, the parallel processing processor 4833 may be pre-programmed with arithmetic processes related to the deep learning algorithm 60, which are implemented by the analysis software 4835a. If the parallel processing processor 4833 is an ASIC, the parallel processing processor 4833 may be pre-programmed with a circuit for executing arithmetic processes related to the deep learning algorithm 60, or may have a programmable module built in addition to such a built-in circuit. The parallel processing processor 4833 is preferably, for example, NVIDIA's GeForce, Quadro, TITAN, or Jetson.

Figure 7 is a diagram explaining the computational processing of the deep learning algorithm 60 of the analysis software 4835a.

The deep learning algorithm 60 is composed of a neural network including multiple hidden layers. The neural network is preferably a convolutional neural network (CNN) with a convolutional layer. The number of nodes in the input layer 60a of the neural network corresponds to the number of sequences contained in the input waveform data of one cell. In the example of Figure 6, the number of nodes in the input layer 60a corresponds to the number of 1024 sequences x 3 of the waveform data 86a, 86b, and 86c of one cell.

The output layer 60b of the neural network has nodes in a number corresponding to the cell types being analyzed. In the example of Figure 8, there are nine cell types being analyzed: "Neutrophils (NEUT)," "Lymphocytes (LYMPH)," "Monocytes (MONO)," "Eosinophils (EO)," "Basophils (BASO)," "Immature Granulocytes (IG)," "Blasts (Blast)," "Abnormal Lymphocytes (Abn LYMPH)," and "NONE." In this case, the number of nodes in the output layer 60b is nine.

When waveform data is input to the input layer 60a of the neural network that constitutes the deep learning algorithm 60, the output layer 60b outputs classification information 82 that classifies cells into multiple cell types, indicating the probability that the cell corresponding to the waveform data corresponds to each cell type. The data output from the output layer 60b includes the cell ID, cell type identification information (label value in Figure 8), and the numerical probability corresponding to each cell type.

The processor 4831 stores the cell classification information 82 obtained by the deep learning algorithm 60 in the RAM 4834. The classification information 82 and characteristic parameter data for each cell are transmitted to the processing unit 300 via the interface section 489.

3. Configuration of Processing Unit 300
The configuration of the processing unit 300 will be described with reference to Fig. 9. The processing unit 300 is connected to the processor 4831 of the measurement unit 400 via an interface unit 3006 and a bus 3003, and can receive the classification information 82 and feature parameter data generated by the measurement unit 400. The interface unit 3006 is, for example, a USB interface.

The processing unit 300 includes a processor 3001, a bus 3003, a memory unit 3004, an interface unit 3006, a display unit 3015, and an operation unit 3016. The processing unit 300 is configured as a general personal computer in terms of hardware, and functions as the processing unit of the cell analysis device 100 by executing a dedicated program stored in the memory unit 3004.

The processor 3001 is a CPU and is capable of executing programs stored in the memory unit 3004.

The memory unit 3004 includes a hard disk drive. The memory unit 3004 stores at least a program for processing the cell classification information 82 transmitted from the measurement unit 400 and generating sample test results.

The display unit 3015 is equipped with a computer screen. The display unit 3015 is connected to the processor 3001 via the interface unit 3006 and bus 3003. The display unit 3015 can receive image signals input from the processor 3001 and display the test results.

The operation unit 3016 is equipped with a pointing device including a keyboard, mouse, or touch panel. By operating the operation unit 3016, users such as doctors and laboratory technicians can input measurement orders into the cell analyzer 100 and input measurement instructions according to the measurement order. The operation unit 3016 can also accept instructions from the user to display test results. By operating the operation unit 3016, the user can view various information related to the test results, such as graphs, charts, and flag information assigned to samples.

4. Operation of the Cell Analysis Device 100
The sample analysis operation by the cell analyzer 100 will be described with reference to FIG.

When the processor 3001 of the processing unit 300 receives a measurement order and measurement instructions from the user via the operation unit 3016, it sends a measurement command to the measurement unit 400 (step S1).

When the processor 4831 of the measurement unit 400 receives the measurement command, it begins measuring the sample. The processor 4831 causes the sample aspirator 450 to aspirate the sample from the blood collection tube T (step S10). Next, the processor 4831 causes the sample aspirator 450 to dispense the aspirated sample into one of the reaction chambers 440a-440e of the sample preparation section 440. The measurement command sent from the processing unit 300 in step S1 includes information about the measurement channel for which measurement is requested by the measurement order. Based on the measurement channel information included in the measurement command, the processor 4831 controls the sample aspirator 450 to dispense the sample into the reaction chamber of the corresponding measurement channel.

The processor 4831 causes the sample preparation unit 440 to prepare a measurement sample (step S11). In step S11, the sample preparation unit 440 receives a command from the processor 4831 and supplies reagents (hemolytic agent and staining solution) to the reaction chamber into which the sample has been ejected, and mixes the sample with the reagents. This results in a measurement sample in which red blood cells are hemolyzed by the hemolytic agent in the reaction chamber and cells targeted by the measurement channel, such as white blood cells and reticulocytes, are stained by the staining agent.

The processor 4831 causes the FCM detection unit 410 to measure the prepared measurement sample (step S12). In step S12, the processor 4831 controls the device mechanism unit 430 to send the measurement sample in the reaction chamber of the sample preparation unit 440 to the FCM detection unit 410. The reaction chamber and the FCM detection unit 410 are connected by a flow path, and the measurement sample sent from the reaction chamber flows through the flow cell 4113 and is irradiated with laser light by the light source 4111 (see Figure 4).

When a measurement sample is supplied to the flow cell 4113, the forward scattered light signal, side scattered light signal, and side fluorescent light signal are input to the A/D conversion unit 482. The A/D conversion unit 482 generates a digital signal that is a collection of waveform data corresponding to individual cells, and feature parameter data that stores the maximum value contained in the waveform data of each cell. The method for generating the waveform data and feature parameters has already been described.

The processor 4831 controls the bus controller 4850 to load the waveform data and feature parameter data generated by the A/D conversion unit 482 into the RAM 4834 via DMA transfer. By DMA transfer, the waveform data is transferred directly to the RAM 4834 without going through the processor 4831. The waveform data is stored in the RAM 4834.

The processor 4831 uses the deep learning algorithm 60 to perform cell classification based on the generated waveform data and generate classification information (step S13).

The processor 4831 transmits the classification information 82 and characteristic parameter data of each cell obtained as a result of step S14 to the processing unit 300 (step S14).

When the processor 3001 of the processing unit 300 receives the classification information 82 and characteristic parameter data from the measurement unit (step S2), it analyzes the classification information 82 using a program stored in the memory unit 3004 and generates test result data for the sample (step S3). The test result data is stored in the memory unit 3004. The processing of step S3 will be described later.

The processor 3001 displays the test results on the display unit 3015 (step S4). The processing of step S4 will be described later.

The processor 3001 transmits the test results to the host computer 500 (step S5). The processing of step S5 will be described later.

The host computer 500 receives the test results sent from the processor 3001 of the processing unit 300 (step S21). This completes the series of processes.

[5. Test result data generation process]
FIG. 11 is a flowchart showing the details of step S3 (test result data generation process) executed by the processor 3001 of the processing unit 300.

The processor 3001 determines the main cell type of the cell based on the classification information 82 received from the measurement unit 400 (step S31). Specifically, the processor 3001 determines the cell type with the highest probability as the main cell type based on the probability for each cell type included in the classification information 82. For example, if the probability data obtained for a certain cell is as follows, the cell type with the highest probability is a neutrophil, and therefore the main cell type of that cell is a neutrophil.

Next, processor 4831 counts the number of cells contained in the specimen for each cell type based on the determined main cell type (step S32). In the processing of step S32, the number of cells is counted for each cell type based on the cell type information of each cell. For example, when WDF is set as the measurement channel, there are nine cell types to be classified, as shown in Figure 8. For example, if there are M cells whose main cell type is neutrophils, processor 3001 generates a count result in which the number of neutrophils is M. Processor 3001 performs similar processing on lymphocytes, monocytes, eosinophils, basophils, immature granulocytes, blasts, and abnormal lymphocytes, and generates a count result for each cell type.

The processor 3001 may further determine whether a sample is normal or abnormal based on the count value of each cell type. For example, because blast cells are not found in the peripheral blood of healthy individuals, if a sample contains a predetermined number or more of cells determined to be blast cells as the main cell type, the sample is suspected of having some kind of abnormality. Furthermore, because immature granulocytes are not normally found in the blood of healthy individuals, if a sample contains a predetermined number or more of cells determined to be immature granulocytes as the main cell type, the sample is also suspected of having some kind of abnormality. The processor 3001 may compare the count value (absolute value) of each cell type with a predetermined numerical range or threshold, and add a flag to the analysis results indicating a suspected abnormality if the count value falls outside the numerical range or exceeds the threshold.

The processor 3001 may further determine the cell content ratio based on the count value of each cell type. For example, when WDF is set as the measurement channel, the processor 3001 determines the ratio of five white blood cell subclasses: neutrophils, lymphocytes, monocytes, eosinophils, and basophils. These five white blood cell subclasses are contained in predetermined proportions in the blood of healthy individuals. The processor 3001 may determine whether the sample is normal or abnormal by comparing the ratio of the white blood cell subclasses with a predetermined numerical range or threshold. The processor 3001 may add a flag to the analysis results indicating that the sample is abnormal if a specific white blood cell ratio falls outside the predetermined numerical range or exceeds the threshold.

The processor 3001 generates the above-mentioned test result data in association with the cell ID (step S33). Figure 12 is a diagram schematically showing a first example of the data structure of the test result data generated by the processor 3001.

The test result data is organized in a relational database and includes multiple data items (columns). In the example shown in Figure 12, the data items include the subject ID, sample ID, measurement result, measurement channel identification code, cell ID, cell type identification information, and the probability for each cell type.

The subject ID data stores the subject ID, which is information used to identify the subject from whom the sample was collected. The subject ID is a multi-digit string consisting of, for example, a combination of numbers and letters.

The sample ID data stores the sample ID, which is information for identifying the sample. The sample ID is, for example, a multi-digit character string consisting of a combination of numbers and letters.

The measurement result data stores the measurement results obtained by the processor 3001 analyzing the measurement data based on the main cell type in step S32. As described above, the measurement result includes the cell count result based on the cell type with the highest probability (main cell type). If the measurement channel is WDF, the measurement result includes the number and percentage of each white blood cell subclass (monocytes, neutrophils, lymphocytes, eosinophils, and basophils). If the measurement channel is RET, the measurement result includes the reticulocyte count. If the measurement channel is WPC, the measurement result includes the hematopoietic progenitor cell count. If the measurement channel is WNR, the measurement result includes the white blood cell count and nucleated red blood cell count.

Measurement channel data stores information indicating the measurement channel used for the measurement of the sample. In the example of the measurement unit 400 of this embodiment, the measurement channels are DIFF, RET, WPC, PLT-F, and WNR. The measurement channel data item stores the character strings "DIFF," "RET," "WPC," "PLT-F," and "WNR" corresponding to the measurement channel used for the measurement. The information indicating the measurement channel may be a character string indicating the measurement channel, or a number or letter assigned to each measurement channel. For example, numbers may be assigned, such as DIFF = 1, RET = 2, WPC = 3, PLT-F = 4, and WNR = 5, and the number corresponding to the measurement channel used may be stored. Measurement channel data stores one or more values for one sample, depending on the number of measurement channels used for the measurement.

Multiple cell ID data are created according to the number of cells detected by measurement on one measurement channel. Cell IDs are natural numbers such as 1, 2, 3, ..., N. The cell ID corresponds to the index added to the beginning of the waveform data corresponding to each cell described above (see Figure 5) and the column order of the feature parameter data (see Figure 6).

Feature parameter data stores the forward scattered light peak (FSCP), side scattered light peak (SSCP), and side fluorescent light peak (SFLP) values of the corresponding cell. In the example of Figure 12, three sets of feature parameter data are created for each cell ID: FSCP, SSCP, and SFLP. As explained with reference to Figure 6, the column order of feature parameter data corresponds to the cell ID. For example, the first column (first column) of the feature parameter data stores feature parameters corresponding to a cell with cell ID "1." Therefore, the FSCP data corresponding to cell ID = 1 stores the value "59" of the first column of the FSCP matrix data among the feature parameter data. Similarly, SSCP and SFLP store the value "30" of the first column of the SSCP matrix data and the value "134" of the first column of the SFLP matrix data, respectively.

Cell type data stores information indicating the cell type being analyzed. Information indicating the cell type is, for example, a label value that can identify the cell type, as shown in Figure 8. Multiple pieces of cell type data are created depending on the number of cell types being analyzed in the measurement channel. For example, as shown in Figure 8, if there are nine types of cell types being analyzed in the measurement channel, including "none," nine pieces of cell type data are created. Note that the identification information may be a character string such as "neutrophil" or "NEUT" instead of a label value.

One piece of probability data is created for each cell type. The probability data stores the probability value output by the deep learning algorithm 60 for the corresponding cell ID and identification information in the form of numerical data. For example, if the classification information 82 of the target cell is as shown in Table 1 above, "90%" is stored in the variable corresponding to neutrophils, "10%" is stored in the variable corresponding to lymphocytes, and "0%" is stored in the variables corresponding to other identification information.

The processor 3001 generates the test result data shown in Figure 12 and stores it in the memory unit 3004.

Figure 13 is a diagram showing a second example of test result data. Compared to Figure 12, the data structure of the test result data shown in Figure 13 includes data that stores a main flag for each piece of identification information. In the data structure of Figure 13, the classification information consists of identification information, a probability, and a main flag.

The main flag data stores a value of 0 or 1. The processor 3001 stores a 1 in the main flag corresponding to the cell type with the highest probability (main cell type), and stores a 0 in the main flags corresponding to other cell types. With this configuration, the processor 3001 does not need to identify the main cell type for each cell based on probability each time main cell type information is used in a calculation or output, thereby speeding up processing. For example, in the scattergram described below, the plots of each cell are displayed in different colors depending on their cell type. In such a case, the processor 3001 can determine the color of the plot of each cell by referencing the main flag, eliminating the need to identify the main cell type based on the probability of each cell. Note that in the second example, when the deep learning algorithm 60 generates the classification information 82, it may also execute a process of setting a numerical value to the main flag along with the classification information 82.

Figure 14 is a diagram showing a schematic representation of a third example of test result data. Compared to Figure 12, the test result data shown in Figure 14 has one main cell type data added for each cell ID. In the case of the data structure in Figure 14, the classification information is composed of identification information, probability, and identification information indicating the main cell type.

The main cell type data stores identification information for the cell type with the highest probability (main cell type). In the second example of Figure 13, it is necessary to search the column in which the main flag is stored to identify the main cell type of each cell, but in the third example of Figure 14, the main cell type can be identified by referencing the main cell type data, further speeding up processing.

Figure 15 is a diagram showing a fourth example of test result data. Compared to Figure 12, the test result data shown in Figure 15 has research flag data added to it corresponding to each piece of identification information. In the case of the data structure in Figure 15, the classification information is composed of identification information, probability, and research flag.

The research flag data stores a value of 0 or 1. The research flag is a flag used to distinguish between cell types displayed as test results for reportable items and cell types displayed as auxiliary research items on the result display screen described below. The processor 3001 stores "1" in the research flag corresponding to a cell type whose probability is below a predetermined threshold, and stores "0" in the research flag for a cell type whose probability is greater than this threshold. When the research flag is set to 1, when test result data is displayed, information indicating that the probability, counting results, etc. for this cell type are preferably used for research purposes can be added.

Figure 16 is a diagram showing a fifth example of test result data. Compared to Figure 12, the test result data shown in Figure 16 has ranking data added to each piece of identification information. In the data structure of Figure 16, the classification information consists of identification information, probability, and ranking.

The ranking data stores numbers from 1 to m (m: the number of cell types being analyzed) assigned to all cell types associated with a cell ID in order of probability. Processor 4831 calculates the ranking in order of probability and stores the calculated ranking in the ranking data for each cell type. In this case, by referencing the ranking data, processor 3001 can smoothly identify not only the cell type with the highest probability, but also, for example, the cell type with the second highest probability.

[6. Test result display process]
FIG. 17 is a flowchart illustrating a first example of the test result display process (step S4) executed by the processor 3001.

The processor 3001 displays the sample list screen 700 (sample explorer screen) (step S410). In response to a user operation, the processor 3001 displays the details screen 800 (browser screen) (step S411). In response to a display instruction from the user, the processor 3001 displays the analysis screen 900 including a scattergram (step S412). The processor 3001 reads out the classification information of the cell corresponding to the dot selected in the scattergram 901 displayed on the analysis screen 900, and displays the read-out classification information (step S413).

Figure 18 shows an example of the sample list screen 700. The sample list screen 700 includes a toolbar 710 and a data display area 720. The toolbar 710 displays multiple icons for performing specific operations on the screen. Specifically, the toolbar 710 includes a sample list screen icon 710a for calling the sample list screen 700 (SE), a details icon 710b for calling the details screen 800, a measurement registration icon 710c for calling the measurement order input screen for entering a measurement order, and a validate icon 710d for validating test results.

Below the toolbar 710 is a data display area 720 where test result data is displayed. The toolbar 710 is always displayed at the top of the screen regardless of the content displayed in the data display area 720. The data display area 720 of the sample list screen 700 displays a sample list display area 931 and a measurement result display area 932.

The sample list display area 931 contains items such as subject ID, sample ID, date and time, etc. The sample list display area 931 displays test result data identified by the subject ID and sample ID in list format.

The measurement result display area 932 displays the measurement results based on the sample ID selected in the sample list display area 931. The measurement results displayed in the measurement result display area 932 are counting results and ratio information based on the main cell type.

When the user selects the details icon 710b on the sample list screen 700 or selects (e.g., double-clicks) a sample record in the sample list display area 931, the details screen 800 is displayed.

Figure 19 shows an example of the details screen 800. The details screen 800 includes a toolbar 810 (710) and a data display area 820. The toolbar 810 is the same as the one displayed on the sample list screen 700. The data display area 820 displays a measurement result area 820a for displaying measurement results, a flag area 820b for displaying flag information, and a graph area 820c for displaying graphs. The measurement result area 820a displays the measurement results of all measurement channels for which measurements were performed on the sample to be displayed. The flag area 820b displays a flag (suspect message) if a flag has been set for the sample based on the measurement results. The graph area 820c displays a graph corresponding to each measurement channel.

In the example of Figure 19, the graph area 820c displays a WDF scattergram, a WNR scattergram, a WPC scattergram, a RET scattergram, a PLT-F scattergram, an RBC histogram, and a PLT histogram. The scattergrams displayed in the graph area 820c are created and displayed based on the above-mentioned characteristic parameters.

If the user wishes to validate the test result data by referring to the analysis results displayed on the details screen 800, they can operate the validate icon 710d. This will validate the test result data. If the test result data is not to be validated, the user can, for example, operate the measurement registration icon 710c to enter a retest order.

The user can display the analysis screen 900 to check the cell classification information. When the user operates (e.g., double-clicks) any graph in the graph area 820c of the details screen 800, the analysis screen 900 pops up and is overlaid on the details screen 800.

Figure 20 is an example of the analysis screen 900. Figure 20 illustrates the analysis screen 900 that is displayed when the user selects a WDF scattergram.

Analysis screen 900 displays a scattergram 901 and a detailed display area 902. The scattergram 901 shown in FIG. 20 is displayed based on the characteristic parameters of cells associated with the measurement channel included in the test result data. In the scattergram 901 shown in FIG. 20, the horizontal axis represents the side scattered light signal peak value (SSCP), and the vertical axis represents the side fluorescent light signal peak value (SFLP). For each cell identified by its cell ID, processor 3001 determines the coordinates in the scattergram 901 based on the SSCP and SFLP, and plots the cell by drawing a dot at the determined coordinates. Processor 3001 performs the same processing on the data for all cells under the measurement channel corresponding to the displayed scattergram 901, and plots the cells on scattergram 901.

Processor 3001 sets the color of the dots based on the classification information of the plotted cells. Specifically, processor 3001 displays cells of different cell types with dots of different colors. For example, in a scattergram 901 corresponding to the WDF channel, neutrophils are displayed in a first color (e.g., blue), lymphocytes in a second color (e.g., purple), monocytes in a third color (e.g., green), eosinophils in a fourth color (e.g., red), and basophils in a fifth color (e.g., yellow).

Conventional white blood cell classification in hemocytometers employs a clustering analysis method in which cells are plotted on a coordinate plane, such as the scattergram 901 shown in FIG. 20, and then classified into multiple populations based on the coordinates of each cell. Therefore, cells plotted at the same coordinates are classified as cells of the same type. In contrast, unlike conventional white blood cell classification, the present embodiment only uses cell characteristic parameters (SSCP and SFLP) to plot cells on the scattergram 901. As described above, cell classification in this embodiment involves analyzing the waveform data of individual cells using the deep learning algorithm 60, thereby identifying individual cell types without relying on clustering techniques. In other words, the coordinates of the cell plots in the scattergram 901 are fragmentary information representing the distribution of cells in the measurement sample based on parameters representing the cell characteristics; the coordinates are not used to identify cell types. Therefore, even if multiple cells are plotted at the same coordinates, these cells may be classified as different cells by the deep learning algorithm 60. When different types of cells are plotted at the same coordinates, the processor 3001 draws dots of a color corresponding to the most prevalent main cell type based on the main cell types of the multiple cells plotted at the same coordinates.

In step S413 of the flowchart in FIG. 17, when the user selects a dot on the scattergram 901 (e.g., by double-clicking), the processor 3001 pops up the detailed display area 902. The detailed display area 902 includes a classification information display area 902a, which displays classification information for the cells included in the selected dot on the scattergram 901. FIG. 20 shows an example in which multiple cell types and the corresponding probabilities for each cell type are displayed. The classification information display area 902a is provided with a scroll bar that allows the display range within the classification information display area 902a to be changed so that all cell types and probabilities can be displayed. If multiple cells are plotted at the selected dot on the scattergram 901, the detailed display area 902 displays a classification information display area 902a for each of the multiple cells. The user can scroll through the classification information by operating the scroll bar.

In the analysis screen 900 of Figure 20, classification information for the cells contained in the selected dot is displayed in a pop-up, but as shown in Figure 21, a classification information display area 902a may also be provided on the analysis screen 900. In this case, the classification information display area 902a is blank until a dot is selected, and once a dot is selected, classification information for the cells corresponding to the selected dot is displayed in the classification information display area 902a.

In the above example, the detailed display area 902 is displayed as a pop-up in response to an operation (e.g., double-clicking) on a dot on the scattergram 901, but the operation for calling up the detailed display area 902 can include various operations as long as it is possible to identify any coordinate on the scattergram 901. For example, the detailed display area 902 may be displayed simply by placing the cursor on the scattergram 901, in which case placing the cursor on the scattergram 901 is the operation for calling up the detailed display area 902. Furthermore, instead of double-clicking, the operation may be a long press, a right click, or pressing a specified key on the keyboard or software keys (e.g., the Enter key) while the cursor is placed on the coordinate.

According to the screens in Figures 20 and 21, the user can check the classification information of the cell corresponding to the dot selected on the scattergram 901 by referring to the classification information display area 902a. Furthermore, by changing the dot selected on the scattergram 901, the user can dynamically switch the display content of the classification information display area 902a and continuously check the display content.

The classification information display area 902a may display other information as classification information, not just the probability of each cell type. For example, if the test result data has the configuration of FIG. 13, the value of the main flag for each cell type may be displayed. If the test result data has the configuration of FIG. 14, a mark may be displayed on the main cell type (the cell type with the highest probability), or the main cell type may be displayed separately. If the test result data has the configuration of FIG. 15, the value of the research flag for each cell type may be displayed. If the test result data has the configuration of FIG. 16, the ranking of each cell type may be displayed.

In Figures 20 and 21, the probability of each cell type is displayed numerically. Alternatively, a graph corresponding to the probability of each cell type may be displayed, as shown in Figures 22 and 23. Figure 22 shows another example of the analysis screen 900. In the example of Figure 22, the classification information display area 902a displays cell probability information for each cell corresponding to the selected dot as a 100% stacked bar chart. This 100% stacked bar chart is displayed corresponding to each cell ID, and the probability values for each identification information are displayed in a stacked format to total 100%. This allows the user to visually grasp the probability of each cell type.

Figure 23 shows another example of the analysis screen 900. The analysis screen 900 in Figure 23 displays a scattergram 901, a detailed display area 902, an enlargement button 903, and an enlarged view 904.

When the user operates the enlargement button 903, a reference range gate 901a is displayed on the scattergram 901. The gate 901a is used to select the desired dot on the scattergram 901 by specifying a range. The user can change the position of the gate 901a on the scattergram 901 by performing an operation such as dragging. The enlarged view 904 displays an enlarged view of the portion of the scattergram 901 within the gate 901a. When the user performs an operation to select a dot on the enlarged view 904, the classification information of the selected cell is displayed in the classification information display area 902a in the detailed display area 902, as in Figures 20 and 21.

According to the screen in Figure 23, the user can easily select the target cell in the enlarged view 904 by setting a gate 901a on the scattergram 901 and displaying the enlarged view 904. This allows the user to smoothly check the classification information even for cells located in an area where cell dots are densely packed in the scattergram 901.

The size of gate 901a may be changed by performing an operation such as dragging on the boundary line of gate 901a. Furthermore, the size of gate 901a may be changed in stages each time the enlargement button 903 is operated.

In the examples of Figures 20 to 23, an example of selecting one dot on the scattergram 901 is shown, but the number of dots that can be selected at a time is not limited to one. For example, multiple dots on the scattergram 901 may be selected by clicking and holding down the cursor on any point on the scattergram 901 and dragging the cursor to specify a range. However, if multiple dots are selected at once, there may be too many cells to display, and displaying the classification information for each cell individually may be difficult for the user to see. For this reason, as will be explained with reference to Figure 29 below, the display method may be switched depending on the number of selected dots. For example, if the number of selected dots is equal to or less than a predetermined threshold, the classification information for the cells may be displayed individually, whereas if the number of selected dots is greater than the predetermined threshold, the classification information for the cells may be displayed statistically rather than individually.

Figure 24 is a flowchart showing a second example of the test result display process. In Figure 24, step S413 has been changed compared to the flowchart in Figure 17. In step S413 in Figure 24, classification information for the cell corresponding to the dot selected in the scattergram 901 is read out and displayed as statistical information on the analysis screen 900.

In the first example of the test result display process in Figure 17, as shown in the examples of Figures 20 to 23, classification information for cells corresponding to selected dots is displayed individually for each cell. Instead, in the examples of Figures 25 to 28 shown below, classification information for multiple cells corresponding to selected dots is aggregated and statistical information is displayed. Examples of the analysis screen 900 are shown in Figures 25 to 28.

The analysis screen 900 in Figure 25 displays a scattergram 901, an enlargement button 903, an enlarged view 904, and a detailed display area 905.

The detailed display area 905 displays statistical information on the probability and number of each cell type in the form of a histogram 905a based on the classification information of all cells within the gate 901a. The histogram 905a is frequency distribution information that aggregates all cells within the gate 901a by the probability of that cell type. In the histogram 905a, the horizontal axis represents probability, and the vertical axis represents the number of cells. The number shown in the histogram 905a also includes the count results of cells that are not the main cell type (cells with a low probability of being that cell type). The detailed display area 905 is provided with a scroll bar that allows the display range within the detailed display area 905 to be changed so that the histograms 905a of all cell types can be displayed.

According to the screen in FIG. 25, by displaying histograms 905a for each cell type in the detailed display area 905, the user can grasp the probability distribution of each cell type for all cells in the gate 901a. For example, with conventional clustering methods using scattergrams, the distribution ranges of multiple cell types may overlap on the scattergram 901. In contrast, according to this embodiment, by setting gate 901a in the range where such distributions overlap and referring to histogram 905a, the frequency distribution of cell types can be confirmed. This allows the user to make detailed judgments about the specimen, such as determining how likely cells of each cell type are to be contained in the range where the distributions overlap.

Figure 26 shows another example of the analysis screen 900. The analysis screen 900 displays a scattergram 901, an enlargement button 903, an enlarged view 904, a cell type selection area 906, and a three-dimensional histogram 907 as statistical information.

The cell type selection area 906 is provided with check boxes for selecting cell types. The cell type selection area 906 is provided with a scroll bar for changing the display range within the cell type selection area 906 so that all cell types can be displayed. The three-dimensional histogram 907 is frequency distribution information for all cells within gate 901a, aggregated by probability of the selected cell type. The three-dimensional histogram 907 displays histograms for each selected cell type in a three-dimensional manner, arranged front to back. In the example of Figure 26, lymphocytes and monocytes have been selected in the cell type selection area 906, and therefore the three-dimensional histogram 907 displays both the lymphocyte histogram and the monocyte histogram.

The analysis screen 900 in FIG. 26 allows the user to simultaneously check the frequency distributions of two or more cell types by selecting two or more cell types and referring to the three-dimensional histogram 907. In the example of FIG. 25, two histograms 905a were displayed simultaneously on the screen, but in the example of FIG. 26, by selecting three or more cell types, three or more histograms are displayed on the screen. In this way, the screen in FIG. 26 allows the user to simultaneously grasp even more frequency distributions than the screen in FIG. 25. In addition, the user can easily compare frequencies for each probability.

Note that the three-dimensional histogram 907 in Figure 26 is a histogram in which multiple histograms 905a shown in Figure 25 are arranged in the depth direction, but instead, the distribution of the probabilities of multiple cell types may be displayed using a surface plot or the like.

Figure 27 shows another example of the analysis screen 900. The analysis screen 900 in Figure 27 displays a scattergram 901, an enlargement button 903, an enlarged view 904, and a count display area 908.

The count display area 908 displays statistical information in the form of a bar graph, tallying the counts of all cells within gate 901a for each cell type with a probability greater than 0%. In this case, because multiple cells within gate 901a are counted overlapping, the total count in count display area 908 exceeds the actual number of cells within gate 901a. For example, suppose gate 901a in Figure 27 contains one cell with probability information of 90% monocyte, 5% neutrophil, and 5% immature granulocyte. In this case, the cell is counted as one count for each of monocyte, neutrophil, and immature granulocyte. In other words, if the probability of belonging to multiple cell types is 0% or greater, the number of that cell is counted overlapping with the number of multiple cell types. The counting method is not limited to this; the number may be counted as a decimal number less than 1 according to the probability. For example, as in the example above, if the reference range of gate 901a contains one cell with probability information of 90% monocytes, 5% neutrophils, and 5% immature granulocytes, the count may be calculated assuming that the range contains 0.9 monocytes, 0.05 neutrophils, and 0.05 immature granulocytes.

If the count result for a cell type is less than a predetermined threshold (e.g., 10), the count value for that cell type is displayed on the bar graph in the number display area 908. In the example of Figure 27, the count value for immature granulocytes was 5, so "5" is displayed on the bar graph for immature granulocytes. In addition, the number display area 908 is provided with a scroll bar that allows the display range within the number display area 908 to be changed so that all cell types can be displayed.

According to the analysis screen 900 in Figure 27, the user can check the number of each cell type in gate 901a by referring to the number display area 908. Cells with a probability exceeding a certain percentage are counted as one, which is convenient when checking the majority and minority cell types. Furthermore, for cell types with small count values in the number display area 908, the count value is displayed as an auxiliary display to confirm the degree of the count value, so the count value of cell types with small count values can be clearly confirmed. This allows the user to smoothly determine the possibility that rare cell types may be contained in the sample.

Note that the count display area 908 displays the count value of cell types with a probability greater than 0%, but the threshold for counting is not limited to 0% and may be set to any value greater than 0% and less than 100%. Also, a user interface that can accept the threshold for counting (for example, the probability selection area 910 in Figure 31) may be provided on the screen.

Figure 28 shows another example of the analysis screen 900. The analysis screen 900 displays a scattergram 901 and a detailed display area 913.

When the user selects a dot on the scattergram 901, statistical information about the cell corresponding to the selected dot is displayed in the detailed display area 913. The detailed display area 913 has areas 913a to 913d. Area 913a displays the top two cell types with the highest probability for one or more cells plotted on the selected dot (hereinafter referred to as the selected cells). Area 913b displays the cell type with the highest probability of being one of the five white blood cell classifications for the selected cell. Area 913c displays the abnormal cell type with the highest probability for the selected cell. Area 913d displays all possible cell types for the selected cell, i.e., cell types with a probability greater than 0%.

According to the analysis screen 900 in Figure 28, the user can understand what cell type the selected cell may be classified into by referring to the detailed display area 913.

The probability of each cell type may also be displayed near the cell type name displayed in areas 913a-913d. If only one cell is selected, the probability information for that cell may be displayed. If multiple cells are selected, a representative value of the probabilities of the cell types of the multiple cells may be calculated and displayed. The representative value may be expressed as, for example, the mean, median, or mode. Furthermore, area 913a may display only the top cell type, or the top three or more cell types. Areas 913b and 913c may display the top two or more cell types. Area 913d may display cell types with probabilities greater than a predetermined threshold.

Figure 29 is a flowchart showing a third example of the test result display process. In step S414, processor 3001 compares the number of cells corresponding to the selected dot on scattergram 901 with a threshold value. If the number of cells is equal to or less than the threshold value, processor 3001 displays the classification information for each cell individually in step S416. The individual display of the classification information for each cell is as described with reference to Figures 20 to 23. If the number of cells is greater than the threshold value, processor 3001 aggregates the classification information and displays statistical information in step S415. The display of the statistical information is as described with reference to Figures 25 to 28.

As mentioned above, when multiple dots are selected at once or a wide gate 901a is set, the number of cells to be displayed may be too large, and displaying the classification information for each cell individually may reduce user visibility. On the other hand, when the number of cells corresponding to the selected dots is small, displaying the classification information for each cell may suit the user's purpose. For example, when the user selects a location far from the center of a cell cluster or a location where cells are sparsely distributed, such as the dot selected on the scattergram 901 in Figure 28, the user may wish to analyze the cells individually rather than as a group. Therefore, in the example of Figure 29, when the number of selected dots is below a predetermined threshold, the classification information for the cells is displayed individually, and when the number of selected dots is greater than the predetermined threshold, statistical information is displayed instead of individual information, automatically providing a screen with high user visibility.

Figure 30 shows a fourth example of the test result display process. In the first to third examples of the test result process described above, the classification information for the cell corresponding to a dot of interest is displayed by selecting the dot of the cell on the scattergram 901. In the examples of Figures 31 to 38 described below, an analysis screen 900 including a graph is displayed (step S417), and dots of cells that meet the defined extraction conditions are highlighted (step S418). In step S418, rather than selecting dots on the scattergram 901, the user defines the conditions for the cells to be extracted, and cells that meet those conditions are extracted and displayed on the graph.

Figure 31 shows another example of the analysis screen 900. The analysis screen 900 displays a scattergram 901, a cell type selection area 909, and a probability selection area 910.

The cell type selection area 909 and probability selection area 910 are configured as so-called pull-down menus. The cell type selection area 909 is configured to allow the user to select one cell type from all cell types corresponding to the measurement channel selected by the user (the WDF channel in Figure 31). The probability selection area 910 displays probability values selectable in 5% increments within the range of 0% to 100% as options. Specifically, the probability selection area 910 is configured to allow the user to select strings that combine probability with an equal or inequality sign, such as "=100%", ">95%, ">90%, ...", ">5%, ">0%", "=0%", etc.

When the user selects a desired cell type in the cell type selection area 909 and a probability in the probability selection area 910, the corresponding dots on the scattergram 901 are highlighted. In the example shown in Figure 31, basophils are selected as the cell type, and ">85%" is selected as the probability. In this case, dots of cells with a basophil probability greater than 85% (dots in area 901b) are highlighted on the scattergram 901. The highlighting can be done by any display method that allows the extracted dots to be distinguished from other dots, such as by displaying the dot in a color different from other dots, highlighting the outline of the dot, or flashing the dot.

The screen in Figure 31 allows the user to define the target cell type and probability as extraction conditions, and thereby ascertain the position of cells that meet the conditions on the scattergram 901. This allows the user to ascertain the position on the conventional scattergram 901 of cells classified according to classification information (cell type and probability), which can be used, for example, to determine the accuracy of classification according to classification information.

Figure 32 shows another example of the analysis screen 900. Instead of the probability selection area 910, the analysis screen 900 in Figure 32 includes a scale bar 910a for selecting a probability value and an extraction target selection area 910c. The scale bar 910a includes a pointer 910b that can slide left and right. By moving the pointer 910b left and right, the user can select any percentage between 0% and 100%. The extraction target selection area 910c displays a selectable inequality sign that can be combined with the percentage selected in the scale bar 910a. For example, as shown in Figure 32, if basophils are selected as the cell type in the cell type selection area 909, the pointer 910b is placed at the 80% position in the scale bar 910a, and ">" is selected in the extraction target selection area 910c, the extraction condition becomes "Probability of basophils > 80%." In the scattergram 901, cell dots that meet the extraction condition "probability of basophils > 80%" are highlighted.

When the user moves the pointer 910b on the scale bar 910a, the numerical range of percentages included in the extraction conditions changes continuously (in stages). When the extraction conditions are changed, the dots extracted on the scattergram 901 change dynamically accordingly.

For example, if the pointer 910b is slid to the right from the state shown in FIG. 32, the extraction condition changes from "Probability of basophils > 80%" to "Probability of basophils > 85%." This change in extraction condition also dynamically changes the cell dots highlighted on the scattergram 901 from "Probability of basophils > 80%" to "Probability of basophils > 85%." In other words, the number of highlighted dots decreases. Conversely, if the pointer 910b is slid to the left, the extraction condition changes from "Probability of basophils > 80%" to "Probability of basophils > 75%." This change in extraction condition also dynamically changes the cell dots highlighted on the scattergram 901 from "Probability of basophils > 80%" to "Probability of basophils > 75%." In other words, the number of highlighted dots increases.

In this way, by sliding the pointer 910b on the scale bar 910a, the extraction conditions can be continuously changed, and the highlighting of the dots of cells that match the extraction conditions displayed on the scattergram 901 changes dynamically accordingly, allowing the user to easily determine where on the scattergram 901 the target cell is located.

Figure 33 shows another example of the analysis screen 900. Figure 33 shows another example of setting extraction conditions. The analysis screen 900 in Figure 33 includes condition setting areas 910d, 910e, and 910f for setting extraction condition 1, extraction condition 2, and extraction condition 3, respectively. The condition setting areas 910d, 910e, and 910f each include pull-down menus corresponding to the cell type selection area 909 and probability selection area 910 shown in Figure 31. Two operator setting areas 910g and 910h are provided between the condition setting areas 910d, 910e, and 910f. Each of the operator setting areas 910g and 910h is configured using a pull-down menu, allowing users to set a logical operator for combining extraction conditions 1 to 3. Options for logical operators include "AND," "OR," and "NOT." In the example in Figure 33, all logical operators are "AND."

Extraction conditions 1 to 3 are defined in each condition setting area 910d, 910e, and 910f, and logical operators are set in operator setting areas 910g and 910h, thereby defining a composite condition in which extraction conditions 1 to 3 are linked by the logical operators. In scattergram 901, dots of cells that satisfy the defined composite condition (dots in area 901b) are highlighted. As in the example of Figure 33, more detailed extraction conditions can be set by extracting cells using composite conditions that combine multiple extraction conditions, rather than just one extraction condition based on a one-to-one combination of cell type and probability.

Figure 34 shows another example of the analysis screen 900. The screen in Figure 34 displays a scattergram 901, a first histogram 910i, a second histogram 910j, and a display area 910k.

In the example shown in Figure 34, only a scattergram 901 is displayed by default on the analysis screen 900. The user can select a specific type of cell population on the displayed scattergram 901. For example, when the user places the cursor over a specific cell population on the scattergram 901, that cell population is selected.

Figure 34 shows a state in which a lymphocyte population is selected. When a specific type of cell population is selected, a first histogram 910i showing the probability distribution of the selected cell population and a second histogram 910j showing the probability distribution of abnormal cells corresponding to the selected cell population are displayed on the analysis screen 900. The first histogram 910i and the second histogram 910j are similar to, for example, the histogram 905a in Figure 25, and show the number of cells on the probability axis. Abnormal cells corresponding to the selected cell population are abnormal cells that are morphologically similar to the selected cell population. Abnormal cells are determined based on a predetermined correspondence relationship depending on the selected cell population. For example, if lymphocytes are selected, the abnormal cells may be abnormal lymphocytes or blasts. For example, if monocytes are selected, the abnormal cells may be blasts. For example, if eosinophils or basophils are selected, the abnormal cells may be immature granulocytes.

In the example of Figure 34, when a specific cell population is selected, cells with a probability of being abnormal cells corresponding to the selected cell population are extracted, and the distribution of these probabilities is displayed as second histogram 910j. By comparing the probability distribution of the selected cell population with the probability distribution of abnormal cells, the user can determine how close the probability distribution of the selected cell population is to 100%. Alternatively, conversely, the user can statistically determine how many cells in the selected cell population have a probability of being abnormal.

The analysis screen 900 in Figure 34 includes a first histogram 910i and a second histogram 910j, as well as a display area 910k that displays an index based on statistical information. For example, the display area 910k displays "The percentage of cells in the lymphocyte population that have the probability of being abnormal lymphocytes is 15% (previous value: 12%)." In other words, the display area 910k displays the percentage of cells with the corresponding probability of being abnormal cells as an index, with the selected cell population as the parent population.

In the example of Figure 34, the sample for the population is defined as "cells that have a probability of being abnormal," but the definition of a sample is not limited to this. For example, it could also be "cells that have the second highest probability of being abnormal." This makes it possible to extract only cells with a higher probability of being abnormal and calculate the index, which may reduce noise. Furthermore, the index is not limited to quantitative indexes such as ratios, but may be qualitative indexes that display levels in stages according to the absolute number of cells that have a probability of being abnormal.

In the example of Figure 34, the number or proportion of cells that correspond to a specific cell population and have a probability of being an abnormal cell can be easily ascertained. While Figures 25 to 28 described above have the advantage of allowing the user to arbitrarily specify a specific cell or cell population on the scattergram 901, it can be difficult to specify only a specific cell population on the scattergram 901. This can be particularly difficult when cells on the periphery of a cluster overlap with adjacent clusters or are distant from the cluster. In the example of Figure 34, simply by placing the cursor over a specific cell population on the scattergram 901, for example, over a lymphocyte population, a cell population with lymphocytes as the main cell can be selected. Furthermore, cells that correspond to lymphocytes and have a probability of being abnormal lymphocytes are automatically extracted, and a probability distribution and index are displayed, allowing the user to easily recognize the extent to which cells that are normal as main cells but have a probability of being abnormal are included.

Figure 35 shows another example of the analysis screen 900. The screen in Figure 35 displays a scattergram 901, a cell type selection area 909, and a histogram 911.

Figure 35 shows an example in which the WPC channel is specified as the measurement channel to be displayed. Therefore, the scattergram 901 is displayed based on the characteristic parameters of all cells corresponding to that measurement channel, and the cell type selection area 909 is configured to allow selection of all cell types associated with the WPC channel, such as mature white blood cells, abnormal lymphocytes, and blasts, as the cell types to be displayed.

When the user selects a cell type in the cell type selection area 909, a histogram 911 is displayed based on the selected cell type. The histogram 911 is frequency distribution information that aggregates the selected cell type by probability. In the histogram 911, the horizontal axis represents probability and the vertical axis represents the number of cells. When the user selects a bar graph on the histogram 911, the corresponding dot (dot in area 901c) on the scattergram 901 is highlighted. In the example shown in Figure 35, abnormal lymphocytes are selected as the cell type, and the bar graph with a probability of 70% is selected. In this case, the dots of cells with an abnormal lymphocyte probability in the 70% range (cells distributed near area 901c) are highlighted on the scattergram 901.

According to the analysis screen 900 in Figure 35, similar to the analysis screens 900 in Figures 31 to 33, the user can select a cell type and probability to determine the location of cells that meet the conditions on the scattergram 901. This allows the user to determine the location on the conventional scattergram 901 of cells that can be classified using the classification information (cell type and probability).

In the examples shown in Figures 31 to 35 above, dots representing cells that satisfy the extraction conditions, which are combinations of cell type and probability, are displayed on a scattergram 901. Instead of the scattergram 901, the number of cells that satisfy the extraction conditions may be displayed using a histogram, as shown in Figures 36 to 38.

The analysis screen 900 in Figures 36 to 38 displays a probability selection area 910 and a number display area 912.

When the user selects a probability in the probability selection area 910 on the analysis screen 900 of Figure 36, the number of cells corresponding to the probability selected in the probability selection area 910, out of all cells corresponding to the target measurement channel, is displayed in the number display area 912 for each cell type. The number display area 912 is provided with a descending order button 912a, which sets the cell types to be sorted from top to bottom in descending order of number, and an ascending order button 912b, which sets the cell types to be sorted from top to bottom in descending order of number. Figure 36 shows the state when the descending order button 912a has been operated, and Figure 37 shows the state when the ascending order button 912b has been operated. Figure 38 shows the state on the screen of Figure 37 with ">20%" set as the probability condition.

According to the screens in Figures 36 to 38, after selecting a probability in the probability selection area 910, the user can set the display of the number display area 912 so that the numbers are sorted in descending order, as shown in Figure 36, by operating the descending order button 912a, or can set the display of the number display area 912 so that the numbers are sorted in ascending order, as shown in Figure 37, by operating the ascending order button 912b. This allows the user to smoothly check, for example, cell types with high or low numbers among the cell types according to the probability of the selected condition.

From the state shown in Figure 37, the user can reselect another probability in the probability selection area 910, and as shown in Figure 38, reconfigure the display in the number display area 912 according to the reselected probability. In this way, by manipulating the probability selection area 910, the number of cell types based on different probability conditions can be smoothly confirmed.

Note that a scattergram 901 may also be provided in Figures 36 to 38. In this case, when a bar graph in the number display area 912 is selected, the cells (points) corresponding to the selected bar graph may be highlighted in the scattergram 901. Also, a histogram 911 (see Figure 35) may also be provided in Figures 36 to 38. In this case, when a bar graph in the number display area 912 is selected, the probability distribution status for the cell type of the selected bar graph may be displayed in the histogram 911.

Figures 39(a) and (b) are schematic diagrams showing other configurations of the scattergram 901.

Scattergram 901 shown in Figure 39(a) has plots of cells according to feature parameters (SSCP, SFLP in Figure 39(a)), overlaid with distribution information of cells according to the probability of each cell type. In this case, the distribution information is composed of stripes connecting cells with the same probability in a contour line pattern, and distribution regions that show ranges of similar probability in shades of gray. In Figure 39(a), the higher the probability, the darker the distribution region is shown in color. Also, in Figure 39(a), cells (dots) based on feature parameters are plotted on scattergram 901, but as in Figure 39(b), dots of cells based on feature parameters do not have to be plotted on scattergram 901.

According to the scattergram 901 shown in Figures 39(a) and (b), cell distribution information according to the probability of each cell type is displayed in addition to the conventional scattergram 901 based on feature parameters. This allows the user to visually understand how the probabilities of cell types are distributed in the conventional scattergram 901.

Figure 40 is a flowchart showing a fifth example of the test result display process. In the first to fourth examples described above, the analysis screen 900 was displayed by double-clicking the scattergram on the details screen 800. In the fifth example, when the "Research" tab is selected on the details screen 800 in Figure 19, the research screen 900R is displayed instead of the analysis screen 900 (step S419). In this example, analysis results based on the main cell type are displayed on the sample list screen 700 and the details screen 800, while information or analysis results related to cell types other than the main cell type are displayed only on the research screen 900R. Then, based on the research flag, classification information for the selected cell type is read and a graph is displayed (step S420).

The screen in Figure 41 is an example of a screen that is displayed when the research information button 921 is operated. The screen in Figure 41 displays a cell type selection area 933 and a number display area 934. The cell type selection area 933 includes check boxes that allow the user to select the cell type related to the specified measurement channel.

When the user selects a cell type in the cell type selection area 933, the count value of the number of cell types with a probability greater than 0% for the selected cell type is displayed in a bar graph in the number display area 934.

In this way, after operating the research information button 921, the user can select a cell type in the cell type selection area 933 on the screen shown in Figure 41 and then refer to the number display area 934 to check the detailed analysis results for the secondary cells. That is, as shown in Figure 41, the research information display mode also displays the numbers of blast cells and abnormal lymphocytes, which normally cannot become main cells, allowing the user to perform more detailed analysis based on the analysis results displayed in the research information display mode.

The screen in Figure 42 is an example of a screen that is displayed when the research information button 921 is operated. The screen in Figure 42 displays a cell type selection area 933, a histogram 935, and a scattergram 936.

When the user selects a cell type in the cell type selection area 933, a histogram 935 is displayed based on the selected cell type. Histogram 935 is frequency distribution information that aggregates all cells corresponding to the measurement channel by probability of the selected cell type. When the user selects a bar graph in histogram 935, the corresponding cell (point) (dot in area 936a) on scattergram 936 is highlighted. In the example shown in Figure 42, blast cells are selected as the cell type, and the bar graph for a probability of 50% is selected. In this case, cells with a blast probability in the 50% range (cells distributed near area 936a) are highlighted on scattergram 936.

In this way, after operating the research information button 921, the user can view more detailed analysis results by selecting a cell type in the cell type selection area 933 on the screen shown in Figure 42 and selecting a bar graph in the histogram 935.

When multiple cell types are selected in the cell type selection area 933 in Figure 42, graphs of the multiple cell types are displayed in histogram 935, similar to the three-dimensional histogram 907 in Figure 26. When multiple bar graphs are selected in histogram 935, all of the corresponding cells (points) on scattergram 936 are highlighted.

7. Host Computer Transmission Process
Next, a description will be given of the data (hereinafter referred to as "output data") sent from the processing unit 300 to the host computer 500. Fig. 43 is a flowchart showing the host computer transmission subroutine.

The processor 3001 of the processing unit 300 receives a validation operation from the user and generates output data to be sent to the host computer 500 in response to the validation operation (step S141).

As explained with reference to Figures 18 and 19, the test result screens, the sample list screen 700 and the details screen 800, display the validate icon 710d, which is displayed commonly across the screens. The user checks the test result screen described above and verifies the test results. If the user determines that the results can be reported to the host computer 500 as a result of the verification, the user clicks the validate icon 710d. If the user does not perform validation on the target sample and wants to schedule a retest for the target subject, the user operates the measurement registration icon 710c to create a test order for the retest.

When the validation operation is performed, the processor 3001 generates output data to be sent to the host computer 500 based on the analysis result data stored in the memory unit 3004 (step S141). The processor 3001 then sends the generated output data to the host computer 500 (step S142).

Figures 44(a) to 45(b) are diagrams showing the structure of output data generated by the processor 3001 and transmitted to the host computer 500. The output data may be constructed by removing classification information corresponding to a specific cell type from the test result data, and may be constructed, for example, as shown in any of Figures 44(a) to 45(b).

The output data shown in Figure 44(a) consists of the specimen ID, measurement results, measurement channel, cell ID, identification information regarding the main cell type, and probability regarding the main cell type. In other words, the output data shown in Figure 44(a) is the test result data shown in Figures 12 to 16 with all data other than the output data shown in Figure 44(a) removed.

The output data shown in Figure 44(b) is composed of the specimen ID, measurement results, measurement channel, cell ID, identification information for the top two cell types with the highest probabilities, and the probabilities for the top two cell types with the highest probabilities. In other words, the output data shown in Figure 44(b) is the output data shown in Figure 44(a) to which identification information and probability for the cell type with the second highest probability have been added. Note that the output data shown in Figure 44(b) is not limited to having the top two cell types and probabilities, and may also have cell types and probabilities from the top down to a predetermined rank.

The output data shown in Figure 45(a) consists of a sample ID and measurement results. In other words, the output data shown in Figure 45(a) is the test result data shown in Figures 12 to 16 with all data other than the output data shown in Figure 45(a) removed. In other words, the output data does not include any information regarding probability.

The output data shown in Figure 45(b) consists of the sample ID, measurement results, and abnormal cell detection information. That is, the output data shown in Figure 45(b) is the same as the output data in Figure 45(a), with abnormal cell detection information added to the measurement results. If a predetermined number of abnormal cells (e.g., abnormal lymphocytes or blasts) with a probability greater than 0% are present in the sample, the fact that the abnormal cells were detected is added to the measurement results as abnormal cell detection information when the test result data is generated. The abnormal cell detection information is, for example, a string of characters such as "Abnormal lymphocytes detected," "Abn LYMPH?," "Blasts detected," or "Blast?" The abnormal cell detection information may also be the cell type and number of abnormal cells detected. The abnormal cell detection information is displayed, for example, in the measurement result display area 932 in Figure 18.

As described above, output data containing the counting results and excluding at least some of the information related to the relationships (such as cell type and probability) is transmitted to the host computer 500. As described above, when probabilities associated with multiple cell types are obtained for each cell, the volume of test result data becomes enormous. Therefore, if all of the test result data were transmitted to the host computer 500, the communication load due to data transmission would be large. In contrast, with the output data shown in Figures 44(a) to 45(b), only the data necessary for management by the host computer 500 is transmitted, rather than transmitting all of the vast amount of test result data. This enables both appropriate data management in the host computer 500 and efficient data transmission.

Feature parameters and scattergrams may be included in the output data shown in Figures 44(a) to 45(b).

In this example, the processor 3001 generates the output data so that it has one of the configurations shown in Figures 44(a) to 45(b). However, the configuration of the output data may also be set in accordance with user instructions.

Figures 46(a) and (b) are diagrams each showing a schematic diagram of the screen configuration for selecting data to be included in the output data.

The screen in Figure 46 (a) displays a cell type selection area 951 and an OK button 952. The cell type selection area 951 has check boxes for selecting a cell type. When the user selects a cell type in the cell type selection area 951 and operates the OK button 952, the processor 3001 of the processing unit 300 stores the settings in the memory unit 3004. From the next time that the processor 3001 generates output data, it includes the set cell type and the probability corresponding to the set cell type in the output data.

The screen in Figure 46 (b) displays a cell range selection area 961 and an OK button 962. The cell range selection area 961 has check boxes for selecting the range of cell types. In the cell range selection area 961, it is possible to select the range of cell types, such as "main cell type only," "sub-cell types other than main cell type," "all cell types," and "do not send." When "main cell type only" is selected, of all the identification information and probabilities, only the identification information and probabilities of the main cell type are included in the output data. When "sub-cell types other than main cell type" is selected, of all the identification information and probabilities, only the identification information and probabilities of all sub-cell types other than the main cell type are included in the output data. When "all cell types" is selected, all the identification information and probabilities are included in the output data. When "do not send" is selected, all the identification information and probabilities are not included in the output data.

When the user selects a range of cell types in the cell range selection area 961 and operates the OK button 962, the processor 3001 stores the settings in the memory unit 3004. From the next time that the processor 3001 generates output data, it will generate output data based on the set range of cell types.

8. Configuration of Processor and Parallel Processing Processor
(Configuration Example 1)
Next, the configuration of the parallel processing processor 4833 mounted on the measurement unit 400 will be described in detail with reference to Fig. 2 again. Below, configuration example 1 will be described regarding the processor 4831 and the parallel processing processor 4833.

As described with reference to FIG. 2, the processor 4831 uses the parallel processing processor 4833 to analyze the waveform data contained in the generated digital signal in accordance with the deep learning algorithm 60. That is, the processor 4831 is programmed to analyze the waveform data contained in the digital signal in accordance with the deep learning algorithm 60. Analysis software 4835a for analyzing cell data based on the deep learning algorithm 60 may be stored in the memory unit 4835. In this case, the processor 4831 executes the analysis software 4835a stored in the memory unit 4835 to perform data analysis based on the deep learning algorithm 60. The processor 4831 is, for example, a CPU (Central Processing Unit). The processor 4831 may be, for example, an Intel Core i9, Core i7, or Core i5, or an AMD Ryzen 9, Ryzen 7, Ryzen 5, or Ryzen 3.

Processor 4831 controls parallel processing processor 4833. Parallel processing processor 4833 executes parallel processing, for example, matrix operations, in accordance with control by processor 4831. In other words, processor 4831 is the master processor of parallel processing processor 4833, and parallel processing processor 4833 is the slave processor of processor 4831. Processor 4831 is also called the host processor or main processor.

The parallel processing processor 4833 executes multiple arithmetic processes in parallel, which are at least part of the processing related to the analysis of waveform data. The parallel processing processor 4833 may be, for example, a graphics processing unit (GPU), a field programmable gate array (FPGA), or an application-specific integrated circuit (ASIC). If the parallel processing processor 4833 is an FPGA, the parallel processing processor 4833 may be pre-programmed with arithmetic processes related to the trained deep learning algorithm 60, for example. If the parallel processing processor 4833 is an ASIC, the parallel processing processor 4833 may be pre-programmed with a circuit for executing arithmetic processes related to the trained deep learning algorithm 60, for example, or may include a programmable module in addition to such a pre-programmed circuit. The parallel processing processor 4833 may be, for example, NVIDIA's GeForce, Quadro, TITAN, Jetson, etc. For the Jetson series, examples include Jetson Nano, Jetson Tx2, Jetson Xavier, and Jetson AGX Xavier.

The processor 4831 performs, for example, calculations related to the control of the measurement unit 400. The processor 4831 performs, for example, calculations related to control signals transmitted and received between the device mechanism 430, the sample preparation unit 440, and the specimen aspirating unit 450. The processor 4831 performs, for example, calculations related to the transmission and reception of information to and from the processing unit 300. The processor 4831 performs, for example, processing related to reading program data from the storage unit 4835, loading the program into the RAM 4834, and transmitting and receiving data to and from the RAM 4834. The above-mentioned processes performed by the processor 4831 are required to be performed in a predetermined order, for example. For example, if the processes required to control the device mechanism 430, the sample preparation unit 440, and the specimen aspirating unit 450 are A, B, and C, they may be required to be performed in the order B, A, and C. Because the processor 4831 often performs sequential processes that depend on such an order, increasing the number of calculation units (sometimes called "processor cores," "cores," etc.) does not necessarily increase processing speed.

On the other hand, the parallel processing processor 4833 executes routine, large-volume computations, such as operations on matrix data containing a large number of elements. In this embodiment, the parallel processing processor 4833 executes parallel processing, which parallelizes at least a portion of the processing for analyzing waveform data according to the deep learning algorithm 60. The deep learning algorithm 60 includes, for example, a large number of matrix operations. The deep learning algorithm 60 may include, for example, at least 100 matrix operations, or may include at least 1,000 matrix operations. The parallel processing processor 4833 has multiple arithmetic units, each of which can execute matrix operations simultaneously. In other words, the parallel processing processor 4833 can execute matrix operations in parallel using multiple arithmetic units as parallel processing. For example, the matrix operations included in the deep learning algorithm 60 can be divided into multiple arithmetic operations that are not order-dependent. The divided arithmetic operations can be executed in parallel by each of the multiple arithmetic units. These arithmetic units are sometimes referred to as "processor cores," "cores," etc.

By performing such parallel processing, it is possible to speed up the calculation processing of the measurement unit 400 as a whole. Processing such as matrix operations included in the deep learning algorithm 60 is sometimes called "single instruction multiple data" (SIMD), for example. The parallel processing processor 4833 is suitable for such SIMD operations, for example. Such a parallel processing processor 4833 is sometimes called a vector processor.

As described above, the processor 4831 is suitable for executing diverse and complex processes. On the other hand, the parallel processing processor 4833 is suitable for executing a large amount of standardized processes in parallel. By executing a large amount of standardized processes in parallel, the TAT (Turn Around Time) required for calculation processing is shortened.

Note that the parallel processing performed by the parallel processing processor 4833 is not limited to matrix operations. For example, when the parallel processing processor 4833 performs learning processing according to the deep learning algorithm 60, differential operations related to the learning processing may be the targets of parallel processing.

The number of arithmetic units in the processor 4831 may be, for example, a dual-core (two cores), a quad-core (four cores), or an octa-core (eight cores). On the other hand, the parallel processing processor 4833 may have, for example, at least 10 arithmetic units (ten cores) and be able to execute ten matrix operations in parallel. Some parallel processing processors 4833 may have, for example, dozens of arithmetic units. Other parallel processing processors 4833 may have, for example, at least 100 arithmetic units (one hundred cores) and be able to execute one hundred matrix operations in parallel. Some parallel processing processors 4833 may have, for example, hundreds of arithmetic units. Other parallel processing processors 4833 may have, for example, at least 1,000 arithmetic units (one thousand cores) and be able to execute one thousand matrix operations in parallel. Some parallel processing processors 4833 may have, for example, thousands of arithmetic units.

Figure 47 shows an example configuration of the parallel processing processor 4833. The parallel processing processor 4833 includes multiple arithmetic units 4836 and a RAM 4837. Each of the arithmetic units 4836 performs arithmetic processing of matrix data in parallel. The RAM 4837 stores data related to the arithmetic processing performed by the arithmetic units 4836. The RAM 4837 is a memory having a capacity of at least 1 gigabyte. The RAM 4837 may also have a capacity of 2 gigabytes, 4 gigabytes, 6 gigabytes, 8 gigabytes, 10 gigabytes, or more. The arithmetic units 4836 obtain data from the RAM 4837 and perform arithmetic processing. The arithmetic units 4836 may be referred to as "processor cores," "cores," etc.

Figures 48 to 50 show an example of mounting the parallel processing processor 4833 on the measuring unit 400. Figures 48 to 50 show an example in which the parallel processing processor 4833 is mounted on the cell analysis device 100 by being incorporated inside the measuring unit 400. Figures 48 and 49 show an example in which the processor 4831 and the parallel processing processor 4833 are provided as separate entities. As shown in Figure 48, the processor 4831 is mounted, for example, on a substrate 4838. The parallel processing processor 4833 is mounted, for example, on a graphics board 4830, which is connected to the substrate 4838 via a connector 4839. The processor 4831 is connected to the parallel processing processor 4833 via a bus 485. As shown in Figure 49, the parallel processing processor 4833 may be mounted, for example, directly on the substrate 4838 and connected to the processor 4831 via the bus 485. Figure 50 shows an example of an implementation in which the processor 4831 and the parallel processing processor 4833 are integrated. As shown in Figure 50, the parallel processing processor 4833 may be built into the processor 4831 mounted on the substrate 4838, for example.

Figure 51 shows another example of mounting the parallel processing processor 4833 on the measurement unit 400. Figure 51 shows an example of mounting the parallel processing processor 4833 on the measurement unit 400 using an external device 4800 connected to the measurement unit 400. The parallel processing processor 4833 is implemented in the external device 4800, which is, for example, a USB (Universal Serial Bus) device, and the parallel processing processor 4833 is mounted on the cell analysis device 100 by connecting this USB device to the bus 485 via the interface unit 487. The USB device may be, for example, a small device such as a USB dongle. The interface unit 487 is, for example, a USB interface having a transfer speed of several hundred Mbps, and more preferably, a USB interface having a transfer speed of several Gbps to several tens of Gbps or more. An example of the external device 4800 equipped with the parallel processing processor 4833 is the Intel Neural Computer Stick 2.

Multiple parallel processing processors 4833 may be installed in the cell analysis device 100 by connecting multiple USB devices equipped with parallel processing processors 4833 to the interface unit 487. Since the number of arithmetic units 4836 of a parallel processing processor 4833 implemented in a single USB device may be smaller than that of a GPU, etc., the number of cores can be scaled up by adding multiple USB devices connected to the measurement unit 400.

Figures 52, 53(a), (b), and 54 show an overview of the calculation processing performed by the parallel processing processor 4833 under the control of analysis software 4835a running on the processor 4831.

Figure 52 shows an example configuration of a parallel processing processor 4833 that performs calculation processing.

The parallel processing processor 4833 has multiple arithmetic units 4836 and a RAM 4837. The processor 4831, which executes the analysis software 4835a, instructs the parallel processing processor 4833 to execute at least a portion of the arithmetic processing required when analyzing waveform data using the deep learning algorithm 60. The processor 4831 instructs the parallel processing processor 4833 to execute arithmetic processing related to waveform data analysis based on the deep learning algorithm. All or at least a portion of the waveform data corresponding to the signal detected by the FCM detection unit 410 is stored in the RAM 4834. The data stored in the RAM 4834 is transferred to the RAM 4837 of the parallel processing processor 4833. The data stored in the RAM 4834 is transferred to the RAM 4837, for example, using a DMA (Direct Memory Access) method. Each of the multiple arithmetic units 4836 of the parallel processing processor 4833 executes arithmetic processing in parallel on the data stored in the RAM 4837. Each of the multiple arithmetic units 4836 obtains the necessary data from RAM 4837 and performs arithmetic processing. Data corresponding to the arithmetic results is stored in RAM 4837 of the parallel processor 4833. The data corresponding to the arithmetic results is transferred from RAM 4837 to RAM 4834, for example, using DMA.

Figures 53(a) and (b) show an overview of the matrix operations performed by the parallel processing processor 4833.

When analyzing waveform data according to the deep learning algorithm 60, matrix multiplication (matrix operation) is performed. The parallel processing processor 4833, for example, executes multiple matrix operation-related calculation processes in parallel. Figure 53(a) shows a formula for calculating matrix multiplication. In the calculation formula shown in (a), matrix c is obtained by multiplying matrix a, which has n rows and n columns, by matrix b, which has n rows and n columns. As shown in Figure 53(a), the calculation formula is written using a multi-level loop syntax. Figure 53(b) shows an example of calculation processes executed in parallel by the parallel processing processor 4833. The calculation formula shown in Figure 53(a) can be divided into n x n calculation processes, where n is the number of combinations of loop variable i in the first level and loop variable j in the second level. Each of the calculation processes divided in this way is independent of each other and can therefore be executed in parallel.

Figure 54 is a conceptual diagram showing that the multiple computational processes illustrated in Figure 53(b) are executed in parallel by the parallel processing processor 4833.

As shown in FIG. 54, each of the multiple arithmetic processes is assigned to one of the multiple arithmetic units 4836 included in the parallel processing processor 4833. Each of the arithmetic units 4836 executes the assigned arithmetic processes in parallel with each other. In other words, each of the arithmetic units 4836 executes the divided arithmetic processes simultaneously.

Calculations by the parallel processing processor 4833 illustrated in Figures 53(a), (b), and 54 determine, for example, information regarding the probability that a cell corresponding to the waveform data belongs to each of multiple cell types. Based on the results of the calculations, the processor 4831 executing the analysis software 4835a performs an analysis regarding the cell type of the cell corresponding to the waveform data. The calculation results are stored in the RAM 4837 of the parallel processing processor 4833 and transferred from the RAM 4837 to the RAM 4834. The processor 4831 transmits the results of the analysis based on the calculation results stored in the RAM 4834 to the processing unit 300 via the bus 485 and the interface section 489.

The calculation of the probability that a cell belongs to each of the multiple cell types may be performed by a processor other than the parallel processing processor 4833. For example, the calculation results by the parallel processing processor 4833 may be transferred from RAM 4837 to RAM 4834, and the processor 4831 may calculate information regarding the probability that a cell corresponding to each waveform data belongs to each of the multiple cell types based on the calculation results read from RAM 4834. Alternatively, the calculation results by the parallel processing processor 4833 may be transferred from RAM 4837 to the processing unit 300, and a processor mounted on the processing unit 300 may calculate information regarding the probability that a cell corresponding to each waveform data belongs to each of the multiple cell types.

In this embodiment, the processing shown in Figures 53(a), (b), and 54 is applied, for example, to computational processing (also called filtering processing) related to the convolutional layer in the deep learning algorithm 60.

Figures 55(a) and (b) show an overview of the computational processing for the convolutional layer.

Figure 55(a) shows an example of waveform data of forward scattered light (FSC) as waveform data input to the deep learning algorithm 60. The waveform data in this embodiment is one-dimensional matrix data, as described with reference to Figure 5. More simply, the waveform data is an array in which elements are arranged in a row. For ease of explanation, the number of elements in the waveform data is assumed to be n (n is an integer greater than or equal to 1). Figure 55(a) shows multiple filters. The filters are generated by the learning process of the deep learning algorithm 60. Each of the multiple filters is one-dimensional matrix data representing the characteristics of the waveform data. The filter shown in Figure 55(a) is matrix data with one row and three columns, but the number of columns is not limited to three. By performing a matrix operation on the waveform data input to the deep learning algorithm 60 and each filter, characteristics corresponding to the cell type related to the waveform data are calculated. Figure 55(b) shows an overview of the matrix operation between the waveform data and the filters. As shown in Figure 55(b), the matrix operation is performed while shifting each filter by one position relative to each element of the waveform data. Matrix operations are calculated using Equation 1 below.

In Equation 1, the subscripts for x are variables that indicate the row and column numbers of the waveform data. The subscripts for h are variables that indicate the row and column numbers of the filter. In the example shown in Figures 55(a) and (b), the waveform data is one-dimensional matrix data, and the filter is matrix data with one row and three columns, so L = 1, M = 3, p = 0, q = 0, 1, 2, i = 0, j = 0, 1, ..., n-1.

The parallel processing processor 4833 executes the matrix operation expressed in Equation 1 in parallel using each of the multiple calculation units 4836. Based on the calculation processing executed by the parallel processing processor 4833, classification information regarding the cell type of each cell is generated. The generated classification information is sent to the processing unit 300, which uses it to generate and display test results for the specimen based on the classification information.

Next, referring to Figure 56, we will explain the cell classification process in step S13 in the flowchart of Figure 10.

The cell classification process in step S13 is performed by the processor 4831 in response to the operation of the analysis software 4835a. The processor 4831 transfers the digital signal taken into RAM 4834 in step S13 to the parallel processing processor 4833 (step S101). As shown in FIG. 52, the processor 4831 transfers the digital signal from RAM 4834 to RAM 4837 by DMA transfer. The processor 4831 controls, for example, the bus controller 4850 to cause the digital signal to be DMA transferred from RAM 4834 to RAM 4837.

The processor 4831 instructs the parallel processing processor 4833 to perform parallel processing on the waveform data contained in the digital signal (step S102). The processor 4831 instructs the parallel processing processor 4833 to perform parallel processing, for example, by calling a kernel function of the parallel processing processor 4833. The processing performed by the parallel processing processor 4833 will be described later in the flowchart illustrated in FIG. 57. The processor 4831 instructs the parallel processing processor 4833 to perform matrix operations related to the deep learning algorithm 60, for example. The digital signal is decomposed into multiple waveform data, which are sequentially input to the deep learning algorithm 60. The indexes corresponding to each cell contained in the digital signal are not input to the deep learning algorithm 60. The waveform data input to the deep learning algorithm 60 is operated on by the parallel processing processor 4833.

The processor 4831 receives the results of the calculations performed by the parallel processing processor 4833 (step S103). The results of the calculations are DMA transferred from RAM 4837 to RAM 4834, for example, as shown in FIG. 52.

The processor 4831 generates analysis results for the cell type of each measured cell based on the calculation results by the parallel processing processor 4833 (step S104).

Figure 57 shows an example of the operation of the parallel processing processor 4833, which is executed based on instructions from the processor 4831 in response to the operation of the analysis software 4835a.

The processor 4831 executing the analysis software 4835a causes the parallel processing processor 4833 to assign arithmetic operations to the arithmetic units 4836 (step S110). The processor 4831 causes the parallel processing processor 4833 to assign arithmetic operations to the arithmetic units 4836, for example, by calling the kernel function of the parallel processing processor 4833. As shown in FIG. 54, for example, a matrix operation related to the deep learning algorithm 60 is divided into multiple arithmetic operations, and each divided arithmetic operation is assigned to the arithmetic unit 4836. Multiple waveform data are input sequentially to the deep learning algorithm 60. The matrix operation corresponding to the waveform data is divided into multiple arithmetic operations and assigned to the arithmetic units 4836.

Each calculation process is performed in parallel by multiple calculation units 4836 (step S111). The calculation process is performed on multiple waveform data.

The calculation results generated by parallel processing by the multiple calculation units 4836 are transferred from RAM 4837 to RAM 4834 (step S112). For example, the calculation results are transferred from RAM 4837 to RAM 4834 by DMA.

(Configuration Example 2)
58 and 59, another configuration example of the cell analyzer 100 configured by the measuring unit 400 and the processing unit 300 will be described. In this configuration example 2, the parallel processor is provided in the processing unit 300.

Figure 58 shows a block diagram of the measurement unit 400 in configuration example 2.

The measurement unit 400 shown in Figure 58 does not include an A/D conversion unit 482, processor 4831, RAM 4834, storage unit 4835, or parallel processing processor 4833, and has the same configuration and functions as the measurement unit 400 of configuration example 1 described in Figures 1 to 57 and the related descriptions, except that it is provided with a connection port 4201. A connection cable 4202 is connected to the connection port 4201.

Figure 59 shows a block diagram of the processing unit 300 in configuration example 2.

As shown in Figure 59, the processing unit 300 includes a processor 3001, a parallel processing processor 3002, a memory unit 3004, a RAM 3005, an interface unit 3006, an A/D conversion unit 3008, a bus controller 4850, and an interface unit 3009, all of which are connected to a bus 3003. In other words, in the example of Figure 59, the parallel processing processor 3002 is incorporated into the processing unit 300 and is installed in the cell analysis device 100.

The bus 3003 is, for example, a transmission path with a data transfer rate of several hundred MB/s or more. The bus 3003 may also be a transmission path with a data transfer rate of 1 GB/s or more. The bus 3003 transfers data based on, for example, the PCI-Express or PCI-X standard. The configurations of the processor 3001, parallel processing processor 3002, memory unit 3004, and RAM 3005, and the processing executed thereby, are similar to the configurations and processing of the processor 4831, parallel processing processor 4833, memory unit 4835, and RAM 4834 described above in Figures 47 to 57. The A/D conversion unit 3008 samples the analog signal output from the measurement unit 400 as described above, and generates a digital signal containing waveform data of the cell. The method of generating the digital signal is as described above.

In the examples of Figures 58 and 59, the connection cable 4202 has, for example, a number of transmission paths corresponding to the types of analog signals transmitted from the measurement unit 400 to the processing unit 300. For example, the connection cable 4202 is configured as a twisted pair cable, and has a number of pairs of wiring corresponding to the types of analog signals transmitted to the processing unit 300. The transmission path from the connection port 3007 to the A/D conversion unit 3008 may also have a number of wiring corresponding to the types of analog signals transmitted to the processing unit 300. In the transmission path from the connection port 3007 to the A/D conversion unit 3008, for example, analog signals are transmitted as differential signals.

As shown in FIG. 60, the processor 3001 and parallel processing processor 3002 have the same configuration and functions as the processor 4831 and parallel processing processor 4833 described above. The parallel processing processor 3002 includes multiple arithmetic units 3200 and RAM 3201. Analysis software 3100 that analyzes the cell type of measured cells runs on the processor 3001. Note that the parallel processing processor 3002 does not need to be directly connected to the bus 3003; for example, it may be implemented in a USB device, and this USB device may be connected to the bus 3003 via an interface unit (not shown), thereby being installed in the cell analysis device 100 as part of the processing unit 300. This USB device may be, for example, a small device such as a USB dongle.

As shown in Figures 58 and 59, the processing unit 300 is connected to the interface unit 489 of the measurement unit 400 via the interface unit 3006. The processing unit 300 transmits control signals for the device mechanism unit 430 and the sample preparation unit 440 to the measurement unit 400 via the interface unit 3006. The interface unit 3006 is, for example, a USB interface.

In the example shown in Figures 58 and 59, the processing unit 300 is connected to the connection port 4201 of the measurement unit 400 via the interface section 3006, the connection port 3007 connected to the A/D conversion section 3008, and the connection cable 4202 connected to the connection port 3007. The connection port 4201 is connected to the analog processing section 420. As described above, the analog signal output from the connection port 4201 to the processing unit 300 is a signal obtained by processing the output of the FCM detection section 410 of the measurement unit 400 by the analog processing section 420. The analog processing section 420 performs processing, including noise removal, on the analog signal input from the FCM detection section 410. The analog signal processed by the analog processing section 420 is transmitted to the processing unit 300 via the connection port 4201 and the connection cable 4202. The connection cable 4202 is configured to be, for example, 1 meter or less in length to reduce noise during signal transmission. The analog signal is transmitted to the processing unit 300 via the connection cable 4202, for example as a differential signal. In this case, the connection cable 4202 is preferably a twisted pair cable.

The processing unit 300 may have multiple connection ports 3007. The processing unit 300 may acquire analog signals from multiple measurement units 400 via the multiple connection ports 3007.

Analog signals transmitted from the measurement unit 400 via the connection cable 4202 are converted into digital signals by the A/D converter 3008 of the processing unit 300. The A/D converter 3008 samples the transmitted analog signals at a predetermined sampling rate (e.g., 1024 sampling points at 10 nanosecond intervals, 128 sampling points at 80 nanosecond intervals, or 64 sampling points at 160 nanosecond intervals), as described with reference to FIG. 5, to generate waveform data for each cell. The waveform data is stored in the memory unit 3004 or RAM 3005 via the bus 3003. The waveform data is transferred to the RAM 3005, for example, by DMA. The processor 3001 and parallel processing processor 3002 perform arithmetic processing on the waveform data stored in the memory unit 3004 or RAM 3005.

Analysis software 3100 running on processor 3001 has the same functions as analysis software 4835a shown in Figure 52. By executing analysis software 3100, processor 3001 generates classification information regarding the cell type of the measured cells through operations similar to those shown in Figures 52, 53(a), (b), 54, 56, 57, and their related descriptions.

In the case of the cell analysis device 100 of configuration example 2 shown in Figures 58 and 59, the generation of waveform data and feature parameters in step S12 of the flowchart shown in Figure 10 and cell classification using a deep learning algorithm in step S13 are performed in the processing unit 300. Step S14 (transmission of classification information) is omitted. The processing in Figures 56 and 57 is performed by the processor 3001 and parallel processing processor 3002 of the processing unit 300.

(Configuration Example 3)
61 and 62 , another configuration example of the cell analyzer 100 configured by the measurement unit 400 and the processing unit 300 will be described. In this configuration example 3, the parallel processing processor 3002 is also installed in the cell analyzer 100 by being incorporated inside the processing unit 300.

Figure 61 shows a block diagram of the measurement unit 400 in configuration example 3.

The measurement unit 400 shown in Figure 61 does not include a processor 4831, RAM 4834, memory unit 4835, or parallel processing processor 4833, and has the same configuration and functions as the measurement unit 400 of configuration example 1 described in Figures 2, 47, and related descriptions, except that it is provided with an interface unit 4851 and a transmission path 4852 for transmitting the digital signal generated by the A/D conversion unit 482 to the processing unit 300.

The interface unit 4851 is, for example, an interface serving as a dedicated line with a communication bandwidth of 1 gigabit/second or more. For example, the interface unit 4851 is an interface compliant with Gigabit Ethernet, USB 3.0, or Thunderbolt 3. If the interface unit 4851 is Gigabit Ethernet, the transmission path 4852 is, for example, a LAN cable. If the interface unit 4851 is USB 3.0, the transmission path 4852 is, for example, a USB cable compliant with USB 3.0. The transmission path 4852 is, for example, a dedicated transmission path for transmitting digital signals between the measurement unit 400 and the processing unit 300.

Figure 62 shows a block diagram of the processing unit 300 in configuration example 3.

The processing unit 300 shown in FIG. 62 has the same configuration and functions as the processing unit 300 of configuration example 2 described in FIG. 59 and its related descriptions, except that it does not include an A/D conversion unit 3008 or a connection port 3007 and includes an interface unit 3010. The processing unit 300 may be connected to multiple measurement units 400 via multiple interface units 3010 and multiple interface units 3006.

The processor 3001 and the parallel processing processor 3002 have the same configuration and functions as the processor 3001 and the parallel processing processor 3002 described in FIG. 60 and its related description. In FIG. 62, the parallel processing processor 3002 does not necessarily need to be directly connected to the bus 3003; for example, it may be implemented in a USB device, and this USB device may be connected to the bus 3003 via an interface unit (not shown). This USB device may be, for example, a small device such as a USB dongle.

Analysis software 3100 running on processor 3001 has the same functions as analysis software 4835a shown in Figure 52. Analysis software 3100 analyzes the cell type of measured cells by performing operations similar to those shown in Figures 52, 53(a), (b), 54, 56, 57, and related descriptions.

In the case of the cell analysis device 100 of configuration example 3 shown in Figures 61 and 62, step S13 (cell classification) of the flowchart shown in Figure 10 is performed in the processing unit 300. Step S14 (transmission of classification information) is omitted. The processing in Figures 56 and 57 is performed by the processor 3001 and parallel processing processor 3002 of the processing unit 300.

In the configurations of Figures 61 and 62, the analog signals (forward scattered light signal, side scattered light signal, side fluorescent light signal) of the cells generated in the FCM detection unit 410 are converted into digital signals in the A/D conversion unit 482 within the measurement unit 400. The digital signals are sent to the processing unit 300 via the interface unit 484, bus 485, interface unit 4851, and transmission path 4852. As described above, the transmission path 4852 is a dedicated transmission path for transmitting digital signals between the measurement unit 400 and the processing unit 300. For example, the measurement unit 400 and the processing unit 300 are connected one-to-one via the transmission path 4852. In other words, the transmission path 4852 is a transmission path that does not involve the transmission of data related to devices other than the components (e.g., the measurement unit 400 and processing unit 300) that make up the cell analysis device 100. The transmission path 4852 is a transmission path that is separate from, for example, an intranet or the Internet. This avoids bottlenecks in the communication speed of digital signal transmission, even when the digital signal generated by A/D conversion within the measurement unit 400 is sent to the processing unit 300.

(Configuration Example 4)
Configuration example 4 of the cell analyzer 100 will be described with reference to Figures 63, 64, 65, 66, and 67.

In this configuration example 4, as illustrated in Figure 63, an analysis unit 600 is provided between the measurement unit 400 and the processing unit 300. That is, in the configurations of Figures 63, 64, 65, 66, and 67, the cell analysis device 100 is configured to include the measurement unit 400, the processing unit 300, and the analysis unit 600. The analysis unit 600 analyzes the cell type of the measured cells. As will be described later, in this configuration example, the parallel processing processor 6002 is installed in the cell analysis device 100 by being incorporated into the analysis unit 600.

Figure 64 illustrates the configuration of the measurement unit 400 in configuration example 4.

The configuration of the measurement unit 400 illustrated in Figure 64 has the same configuration and functions as the measurement unit 400 of configuration example 3 described in Figure 61 and its related description. An analysis unit 600 is provided between the measurement unit 400 and the processing unit 300. The analysis unit 600 may be connected to multiple measurement units 400. The analysis unit 600 may be connected to multiple processing units 300. The interface unit 4851 is, for example, an interface having a communication bandwidth of 1 gigabit/second or more. For example, the interface unit 4851 is an interface compliant with Gigabit Ethernet, USB 3.0, or Thunderbolt 3. If the interface unit 4851 is Gigabit Ethernet, the transmission path 4852 is, for example, a LAN cable. If the interface unit 4851 is USB 3.0, the transmission path 4852 is a USB cable compliant with USB 3.0. As described above, the transmission path 4852 is a dedicated transmission path for transmitting digital signals between the measurement unit 400 and the processing unit 300. For example, the measurement unit 400 and the processing unit 300 are connected one-to-one via the transmission path 4852.

Figure 65 shows an example configuration of the analysis unit 600.

The analysis unit 600 includes, for example, a processor 6001, a parallel processing processor 6002, a bus 6003, a memory unit 6004, a RAM 6005, an interface unit 6006, and an interface unit 6007, which are connected to the bus 6003. The bus 6003 is, for example, a transmission path with a data transfer rate of several hundred MB/s or more. The bus 3003 may also be a transmission path with a data transfer rate of 1 GB/s or more. The bus 3003 transfers data based on, for example, the PCI-Express or PCI-X standard. The analysis unit 600 may be connected to multiple measurement units 400 via multiple interface units 6006. When multiple measurement units 400 are provided, the analysis unit 600 may be connected to each of the measurement units 400 (for example, multiple measurement units 400 and multiple analysis units 600 are connected one-to-one).

As shown in FIG. 66, the processor 6001 and the parallel processing processor 6002 have the same configuration and functions as the processor 4831 and the parallel processing processor 4833 described above. The parallel processing processor 6002 includes multiple arithmetic units 6200 and RAM 6201. Analysis software 6100 that analyzes the cell type of measured cells runs on the processor 6001. The analysis software 6100 running on the processor 6001 has the same functions as the analysis software 4835a shown in FIG. 52. The analysis software 6100 analyzes the cell type of measured cells by operations similar to those shown in FIGS. 52, 53(a), (b), 54, 55(a), (b), 56, 57, and related descriptions. The analysis software 6100 transmits classification information of the measured cells to the processing unit 300 via the interface unit 6007. The interface unit 6007 is, for example, Ethernet (registered trademark) or USB. The interface unit 6007 may be an interface capable of wireless communication (e.g., Wi-Fi (registered trademark), Bluetooth (registered trademark)).

Figure 67 shows the configuration of the processing unit 300 in configuration example 4.

The processing unit 300 shown in FIG. 67 may not include a parallel processing processor 3002 like the processing unit 300 shown in FIG. 59 and FIG. 62. Furthermore, the analysis software 3100 shown in FIG. 59 and FIG. 62 may not be running on the processor 3001 shown in FIG. 67. The processing unit 300 receives the analysis results from the analysis unit 600 via the interface unit 3006. The interface unit 3006 is, for example, Ethernet or USB. The interface unit 3006 may also be an interface capable of wireless communication (for example, Wi-Fi or Bluetooth).

In the configurations of Figures 64, 65, 66, and 67, the analog cell signals (forward scattered light signal, side scattered light signal, side fluorescent light signal) generated in the FCM detection unit 410 are converted to digital signals in the A/D conversion unit 482 within the measurement unit 400. Waveform data is sent to the analysis unit 600 via the interface unit 484, bus 485, interface unit 4851, and transmission path 4852. As described above, the interface unit 4851 is a dedicated interface for connecting the measurement unit 400 and the analysis unit 600, and provides a one-to-one connection between the measurement unit 400 and the analysis unit 600. In other words, the transmission path 4852 is a transmission path that does not involve the transmission of data related to devices other than the components that make up the cell analysis device 100 (e.g., the measurement unit 400 and processing unit 300). The transmission path 4852 is a transmission path that is separate from, for example, an intranet or the Internet. This avoids bottlenecks in the communication speed of digital signal transmission, even when the digital signal generated by A/D conversion within the measurement unit 400 is sent to the processing unit 300.

In the case of the cell analysis device 100 of configuration example 4 shown in Figures 64, 65, 66, and 67, step S13 (cell classification) and step S14 (transmission of classification information) of the flowchart shown in Figure 10 are performed in the analysis unit 600. That is, the digital signal generated in step S13 is transmitted from the measurement unit 400 to the analysis unit 600, and the classification information is transmitted to the processing unit 300 in step S14. The processing in Figures 56 and 57 is performed by the processor 6001 and parallel processing processor 6002 of the analysis unit 600.

(Configuration Example 5)
A fifth configuration example of the cell analyzer 100 will be described with reference to FIGS. 63, 67, and 68.

Like the aforementioned configuration example 4, the cell analysis device 100 of configuration example 5 is configured with a measurement unit 400, a processing unit 300, and an analysis unit 600. The measurement unit 400 of configuration example 5 shown in FIG. 68 has the same function and configuration as the measurement unit 400 described in FIG. 58 and its related description. The measurement unit 400 of configuration example 5 shown in FIG. 68 is connected to the analysis unit 600 via a connection cable 4202. For example, the connection cable 4202 is configured as a twisted pair cable and has a number of wiring pairs corresponding to the type of analog signal transmitted to the processing unit 300. The connection cable 4202 is configured with a length of, for example, 1 meter or less to reduce noise during signal transmission. The measurement unit 400 transmits analog signals to the analysis unit 600 via the connection cable 4202.

The analysis unit 600 shown in Figure 69 has the same functions and configuration as the analysis unit 600 described in Figure 65 and its related descriptions. That is, in the example of Figure 69, a parallel processing processor 6002 is mounted on the cell analysis device 100 so as to be incorporated into the analysis unit 600. The analysis unit 600 shown in Figure 69 further includes a connection port 6008 and an A/D conversion unit 6009. Analog signals transmitted from the analysis unit 600 via the connection cable 4202 are input to the A/D conversion unit 6009 via the connection port 6008. The A/D conversion unit 6009 converts the analog signals into digital signals using processing similar to that of the A/D conversion unit 482.

The analysis unit 600 may be connected to multiple measurement units 400 via multiple connection ports 6008. If multiple measurement units 400 are provided, the analysis unit 600 may be connected to each of the measurement units 400 (for example, multiple measurement units 400 and multiple analysis units 600 are connected one-to-one).

As shown in FIG. 66, processor 6001 and parallel processing processor 6002 have the same configuration and functions as processor 4831 and parallel processing processor 4833 described above. Analysis software 6100 runs on processor 6001 to analyze the cell type of measured cells. Analysis software 6100 running on processor 6001 has the same functions as analysis software 4835a shown in FIG. 52. Analysis software 6100 analyzes the cell type of measured cells by performing operations similar to those shown in FIGS. 52, 53(a), (b), 54, 55(a), (b), 56, 57, and their related descriptions. Analysis software 6100 transmits the analysis results of the cell type of measured cells to processing unit 300 via interface unit 6007. Interface unit 6007 is, for example, Ethernet or USB. Interface unit 6007 may also be an interface capable of wireless communication (e.g., Wi-Fi, Bluetooth).

In the case of the cell analysis device 100 of configuration example 5 shown in Figures 68 and 69, the generation of waveform data and characteristic parameters in step S12 of the flowchart shown in Figure 10, step S13 (cell classification), and step S14 (transmission of classification information) are performed in the analysis unit 600. That is, waveform data generation (step S12) is performed in the A/D conversion section 6009 of the analysis unit 600, cell classification based on the digital signal (step S13) is performed by the processor 6001 and parallel processing processor 6002 of the analysis unit 600, and classification information is transmitted from the analysis unit 600 to the processing unit 300 (step S14). The processing in Figures 56 and 57 is performed by the processor 6001 and parallel processing processor 6002 of the analysis unit 600.

9. Neural Network Structure and Training of Deep Learning Algorithm 60
(Neural network structure)
FIG. 70( a) illustrates the structure of a convolutional neural network that implements the deep learning algorithm 60.

The neural network comprises an input layer 60a, an output layer 60b, and an intermediate layer 60c between the input layer 60a and the output layer 60b, with the intermediate layer 60c being composed of multiple layers. The number of layers making up the intermediate layer 60c can be, for example, 5 or more, preferably 50 or more, and more preferably 100 or more.

In a neural network, multiple nodes 89 are arranged in layers and connected between layers. This allows information to propagate in only one direction, as indicated by arrow D in the figure, from the input layer 60a on the input side to the output layer 60b on the output side.

(Operation at each node)
FIG. 70(b) is a schematic diagram showing the calculations at each node.

Each node 89 receives multiple inputs and calculates one output (z). In the example shown in Figure 70(b), node 89 receives four inputs. The total input (u) received by node 89 is expressed, for example, by the following (Equation 2). In this embodiment, one-dimensional row and column data is used as training input data and analysis input data, so if the variables in the arithmetic formula correspond to two-dimensional matrix data, a process is performed to convert the variables so that they correspond to one-dimensional matrix data.

Each input is multiplied by a different weight. In (Equation 2), b is a value called the bias. The output (z) of the node is the output of a predetermined function f for the total input (u) expressed in (Equation 2), and is expressed in (Equation 3) below. The function f is called the activation function.

Figure 70(c) is a schematic diagram showing operations between nodes.

In a neural network, nodes are arranged in layers, and each node 89 outputs a result (z) expressed in (Equation 3) in response to the total input (u) of each node 89, expressed in (Equation 2). The output of a node in the previous layer becomes the input to a node in the next layer. In the example shown in Figure 70(c), the output of node 89a in the layer on the left side of the figure becomes the input to node 89b in the layer on the right side of the figure. Each node 89b receives the output from node 89a. A different weight is applied to each connection between each node 89a and each node 89b. If the outputs of each of the multiple nodes 89a are x1 to x4, the inputs to each of the three nodes 89b are expressed by the following (Equation 4-1) to (Equation 4-3).

Generalizing these (Equation 4-1) to (Equation 4-3), we obtain the following (Equation 4-4), where i = 1, ..., I, j = 1, ..., J. I is the total number of inputs, and J is the total number of outputs.

Applying (Equation 4-4) to the activation function gives the output. The output is expressed as (Equation 5) below.

(activation function)
In the cell type analysis method according to the embodiment, a rectified linear unit function is used as the activation function. The rectified linear unit function is expressed by the following (Equation 6).

(Equation 6) is a linear function of z = u, where the part of u<0 is set to u=0. In the example shown in Figure 70(c), the output of node j=1 is expressed by the following equation using (Equation 6):

(Neural network learning)
If a function expressed using a neural network is defined as y(x:w), the function y(x:w) will change when the parameter w of the neural network is changed. Adjusting the function y(x:w) so that the neural network selects a parameter w that is more suitable for the input x is called neural network training. Suppose multiple pairs of input and output of a function expressed using a neural network are given. If the desired output for a certain input x is d, the input/output pairs are given as {(x1, d1), (x2, d2), ..., (xn, dn)}. The set of pairs represented by (x, d) is called training data.

Learning a neural network means adjusting the weight w using an error function so that, for any input/output pair (xn, dn), when input xn is given, the neural network output y(xn:w) comes as close as possible to output dn.

An error function is a measure of the closeness between a function expressed using a neural network and training data. The error function is also called a loss function. The error function E(w) used in the cell type analysis method according to the embodiment is expressed by the following equation (7). Equation (7) is called cross entropy.

A method for calculating the cross entropy of (Equation 7) will be described. In the output layer of the neural network used in the cell type analysis method according to the embodiment, i.e., in the final layer of the neural network, an activation function is used to classify the input x into a finite number of classes according to its content. The activation function is called a softmax function and is expressed by the following (Equation 8). It is assumed that the output layer 60b has the same number of nodes as the number of classes k. The total input u of each node k (k = 1, ..., K) in the output layer L is given by uk ^(L) from the output of the previous layer L-1. As a result, the output of the kth node in the output layer is expressed by the following (Equation 8).

(Equation 8) is the softmax function. The sum of the outputs y1, ..., yk determined by (Equation 8) is always 1.

If each class is represented as C1, ..., Ck, the output yk (i.e., uk ^(L) ) of node k in the output layer L represents the probability that a given input x belongs to class Ck. The input x is classified into the class with the maximum probability expressed by the following (Equation 9).

When training a neural network, the function represented by the neural network is considered to be a model of the posterior probability of each class, and under such a probability model, the likelihood of the weight w for the training data is evaluated, and the weight w that maximizes the likelihood is selected.

The target output dn from the softmax function in (Equation 8) is set to 1 only if the output is the correct class, and to 0 otherwise. If the target output is expressed in vector form as dn = [dn1, ..., dnk], for example, if the correct class of input xn is C3, only the target output dn3 will be 1, and all other target outputs will be 0. When encoded in this way, the posterior distribution is expressed as (Equation 10) below.

The likelihood L(w) of weight w for training data {(xn, dn)} (n = 1, ..., N) is expressed as follows (Equation 11). Taking the logarithm of the likelihood L(w) and inverting the sign yields the error function of (Equation 7).

Learning refers to minimizing the error function E(w) calculated based on training data with respect to the parameter w of the neural network. In the cell type analysis method according to the embodiment, the error function E(w) is expressed by (Equation 7).

Minimizing the error function E(w) with respect to the parameter w is equivalent to finding a local minimum of the function E(w). The parameter w is the weight of the connection between nodes. The minimum of the weight w is found by iterative calculations that start with an arbitrary initial value and repeatedly update the parameter w. An example of such calculations is the gradient descent method.

Gradient descent uses the vector expressed in the following equation (12).

In gradient descent, the process of moving the current value of parameter w in the negative gradient direction (i.e., -∇E) is repeated many times. If the current weight is w ^(t) and the weight after the movement is w ^(t+1) , the calculation using gradient descent is expressed by the following (Equation 13). The value t represents the number of times the parameter w has been moved.

The symbol shown in the following (Equation 14), which was used in the above (Equation 13), is a constant that determines the magnitude of the update amount of the parameter w, and is called the learning coefficient.

By repeating the calculation expressed by (Equation 13), the error function E(w ^(t) ) decreases as the value t increases, and the parameter w reaches a minimum point.

Note that the calculation using (Equation 13) may be performed on all training data (n = 1, ..., N), or on only a portion of the training data. Gradient descent performed on only a portion of the training data is called stochastic gradient descent. The cell type analysis method according to the embodiment uses stochastic gradient descent.

Using Figure 71 as an example, an overview of training the deep learning algorithm 60 will be explained. As described above, the deep learning algorithm 60 is composed of a neural network including multiple intermediate layers. As training data, waveform data of cells whose cell types have been previously identified is input to the input layer 60a of the neural network. As training data, probability data 78 is input to the output layer 60b of the neural network. If the number of cell types to be classified by the deep learning algorithm 60 is nine, as shown in Figure 8, the output layer 60b will have nine nodes, and each node is assigned a cell type. The probability data 78 is a data group in which the probability of the label value of the previously identified cell type corresponding to the waveform data input to the input layer 60a is 100% and the probability of other cell types is 0%. In this way, training data is input to the input layer 60a and output layer 60b, respectively, and the neural network is trained.

10. Effects of the embodiment
According to the above-described embodiment, the results of classifying individual cells into multiple cell types are displayed, as representatively illustrated in, for example, FIG. 20 . In conventional cell classification methods, cells with characteristics of, for example, neutrophils or immature granulocytes are classified alternatively into one of these types. However, in the above-described embodiment, the probability that such cells belong to each of the neutrophils and immature granulocytes is calculated using a deep learning algorithm, and the probability values corresponding to each cell type are displayed. The user can then take further analytical action, such as retesting or visually inspecting a sample containing cells that have a probability of being abnormal, in order to detect abnormal cells.

60 Deep learning algorithm 86a, 86b, 86c Waveform data 100 Cell analysis device (analysis device)
410 FCM detection unit (detection unit)
440 Sample preparation unit 482 A/D conversion unit (signal processing unit)
901 Scattergram (graph)
3001, 4831 Processor 3002, 4833 Parallel processing processor 3015 Display unit 4113 Flow cell

Claims (18)

An analytical method for analyzing a sample containing cells, comprising:
Irradiating a measurement sample prepared from the specimen with light and detecting light emitted from the cells;
acquiring characteristic data of each of a plurality of cells contained in the specimen based on the detected light;
By analyzing the feature data using a deep learning algorithm, a probability that each of the cells corresponds to each of a plurality of cell types is determined, and a first cell type and a second cell type are determined based on the probability;
generating a first analysis result by counting and/or classifying a plurality of cells contained in the specimen based on the first cell type;
generating a second analysis result based on the second cell type;
An analytical method characterized by: the first cell type has the highest probability,
The second cell type has a probability equal to or less than a predetermined threshold .
The analytical method according to claim 1 . providing the second analysis result in a manner different from the first analysis result;
The analytical method according to claim 1 . An analytical device for analyzing a sample containing cells,
a sample preparation unit that prepares a measurement sample from the specimen;
a detection unit that irradiates the measurement sample prepared by the sample preparation unit with light and detects light emitted from cells;
a signal processing unit that acquires characteristic data of each of a plurality of cells contained in the specimen based on the detected light;
A display unit;
an information processing unit including at least one processor;
The processor:
By analyzing the feature data using a deep learning algorithm, a probability that each of the cells corresponds to each of a plurality of cell types is determined, and a first cell type and a second cell type are determined based on the probability;
generating a first analysis result by counting and/or classifying a plurality of cells contained in the specimen based on the first cell type;
generating a second analysis result based on the second cell type;
An analytical device characterized by: the first cell type has the highest probability,
The second cell type has a probability equal to or less than a predetermined threshold .
The analytical device according to claim 4 . providing the second analysis result in a manner different from the first analysis result;
The analytical method according to claim 4. The analysis device of claim 4, wherein the multiple cell types include at least one normal cell and at least one abnormal cell. The analyzer of claim 4, wherein the plurality of cell types includes lymphocytes, monocytes, eosinophils, neutrophils, basophils, and abnormal blood cells. The analyzer of claim 8, wherein the abnormal blood cells include at least one of immature granulocytes, blast cells, and abnormal lymphocytes. The analysis device described in any one of claims 4 to 9, wherein the deep learning algorithm is trained using cell characteristic data and information about the cell type as training data. the detection unit detects light generated when cells pass through a flow cell irradiated with light;
The analyzer according to any one of claims 4 to 10, wherein the signal processing unit acquires a waveform signal that changes over time in response to the detected light. The analysis device according to claim 11 , wherein the feature data is waveform data generated by sampling the waveform signal at multiple points in time. The analysis device described in any one of claims 4 to 12, wherein the information processing unit analyzes the feature data using the processor and a parallel processing processor operating under the instructions of the processor. the information processing unit displays a graph plotting the plurality of cells;
The analytical device according to any one of claims 4 to 13, further comprising displaying information based on the probability of one or more cells selected in response to the selection of a plot corresponding to the selected cells in the graph. The analysis device described in any one of claims 4 to 14, wherein the information processing unit displays statistical information generated based on the probability. The information processing unit
displaying a graph plotting a plurality of cells contained in the specimen based on the characteristic data;
An analytical device according to any one of claims 4 to 15, which displays information based on the probability of one or more selected cells in response to the selection of a plot corresponding to one or more cells in the graph. The information processing unit
displaying a graph plotting a plurality of cells contained in the specimen based on the characteristic data;
The analytical device according to any one of claims 4 to 16, further comprising displaying statistical information based on the probabilities of a plurality of selected cells in response to the selection of the plurality of cells in the graph. The information processing unit
displaying a graph plotting a plurality of cells contained in the specimen based on the characteristic data;
Accepts input of cell extraction conditions,
extracting cells that satisfy the extraction conditions for the cell type;
The analysis device according to any one of claims 4 to 17, wherein the extracted cells are displayed in a manner that distinguishes them from other cells among the cells displayed on the graph.