JP5486933B2 - Medical image processing device - Google Patents

Description

  The present invention relates to a medical image processing apparatus.

  Conventionally, in the treatment of a brain tumor, in order to improve various local symptoms such as motor paralysis, speech disorder, and consciousness disorder caused by the brain tumor, it is required to perform an operation for removing the brain tumor at an early stage. In brain tumor surgery, it is important to clearly grasp the positional relationship between the brain tumor and the surrounding brain functional areas so as not to worsen local symptoms during the surgery. Therefore, for example, when a surgical plan is examined before surgery, a clinical application that combines and displays a brain tumor morphological image and a brain functional image is used.

  Examples of such clinical applications include MPR (Multi Planar Reconstruction) images of morphological images generated by an MRI (Magnetic Resonance Imaging) apparatus, functional images obtained by fMRI (Functional MRI) and Wada-test. Is known (see Non-Patent Document 1). Here, fMRI is a method of visualizing the hemodynamic response related to brain activity using an MRI apparatus. The Wada test is a test method for diagnosing whether the left or right brain has language function.

Christoph Stippich, “Clinical Functional MRI: Presurgical Functional Neuroimaging”, 1st edition, (Germany), Springer-Verlag, 2007 April 4, p. 115-127

  However, in the conventional technique described above, the type of functional image to be displayed on the clinical application is generally selected by the operator. Therefore, the operator needs to select a functional image suitable for examining the surgical plan after analyzing the position and nature of the brain tumor using a brain function map and various functional images before the operation. These operations are very time-consuming and are a heavy burden on the operator. As a result, according to the conventional technique, the operator cannot easily make an operation plan for a brain tumor.

  The present invention has been made in view of the above, and an object of the present invention is to provide a medical image processing apparatus capable of assisting an operator so that an operation plan for a brain tumor can be easily created. .

In order to solve the above-described problems and achieve the object , the present invention provides a medical image processing device that identifies a tumor position based on a brain morphological image, and a tumor position identifying unit that identifies the position of the brain tumor. A tumor property determination unit that determines the property of the brain tumor based on the position of the brain tumor that has been performed, a functional image selection unit that selects a type of functional image of the brain based on a determination result of the tumor property determination unit, and the functional image And a functional image generating means for generating the type of functional image selected by the selecting means.

According to the present invention, there is an effect that it is possible to assist an operator so that an operation plan for a brain tumor can be easily made.

FIG. 1 is a functional block diagram illustrating the configuration of the medical image processing apparatus according to the present embodiment. FIG. 2 is a diagram illustrating an example of a tumor property table stored in the tumor property table storage unit. FIG. 3 is a diagram illustrating an example of a functional image table stored in the functional image table storage unit. FIG. 4 is a diagram illustrating an example of a composite image displayed by the image display control unit. FIG. 5 is a flowchart illustrating a processing procedure of processing executed by the medical image processing apparatus according to the present embodiment.

  Embodiments of a medical image processing apparatus according to the present invention will be described below in detail with reference to the drawings. In addition, this invention is not limited by the Example shown below.

  First, an outline of the medical image processing apparatus according to the present embodiment will be described. The medical image processing apparatus according to the present embodiment specifies the position of the brain tumor based on the morphological image of the brain. The medical image processing apparatus determines whether or not the brain tumor is malignant based on the identified position of the brain tumor, and selects the type of functional image of the brain based on the determination result. Then, the medical image processing apparatus generates a selected type of functional image.

  That is, the medical image processing apparatus according to the present embodiment identifies the position of the brain tumor from the brain morphological image, and automatically selects the type of functional image according to the nature of the identified brain tumor. Therefore, according to this medical image processing apparatus, it is possible to assist the operator so that an operation plan for a brain tumor can be easily made.

  The medical image processing apparatus will be specifically described below. First, the configuration of the medical image processing apparatus according to the present embodiment will be described. FIG. 1 is a functional block diagram illustrating a configuration of a medical image processing apparatus 10 according to the present embodiment. As illustrated in FIG. 1, the medical image processing apparatus 10 includes an input unit 11, a display unit 12, a storage unit 13, and a control unit 14.

  The input unit 11 receives various instructions and information input from the operator. The input unit 11 is, for example, a pointing device such as a mouse or a trackball, or an input device such as a keyboard.

  The display unit 12 displays various images referred to by the operator, a GUI (Graphical User Interface) for receiving various operations from the operator, and the like. The display unit 12 is, for example, a liquid crystal monitor or a CRT (Cathode Ray Tube) monitor.

  The storage unit 13 stores programs and data necessary for various processes executed by the control unit 14 described later. The storage unit 13 stores various images generated by the control unit 14. The storage unit 13 is, for example, an HDD (Hard Disc Drive), a DVD (Digital Versatile Disk) drive, or a semiconductor memory.

  As shown in FIG. 1, the storage unit 13 includes a brain function map storage unit 13a, a tumor property table storage unit 13b, a functional image table storage unit 13c, a composite image storage unit 13d, and an extraction rate storage unit 13e. Have

  The brain function map storage unit 13a stores information relating to a brain map in which a brain region is divided into a plurality of areas (hereinafter referred to as brain function areas) for each function. For example, the brain function map storage unit 13a stores information related to Broadman's brain map.

  The tumor property table storage unit 13b stores a tumor property table in which the brain function areas classified by the brain map are associated with the properties of brain tumors that can occur in each brain function area. FIG. 2 is a diagram illustrating an example of a tumor property table stored in the tumor property table storage unit 13b. As shown in FIG. 2, the tumor property table holds, for each brain function area, data that associates the brain function area with information on a brain tumor that can occur in the brain function area. The brain tumor information here includes the name, frequency, and nature of the brain tumor.

  In the brain function area, information representing each brain function area divided in the brain map stored by the brain function map storage unit 13a is set. For example, “central previous”, “primary motor area”, “somatosensory association area”, and the like are set in the brain function area. In the brain tumor name, information indicating the name of the brain tumor is set. For example, “glioma”, “malignant lymphoma”, “germ cell tumor” or the like is set as the brain tumor name.

  In the occurrence frequency, information indicating the occurrence frequency of the brain tumor is set. For example, a numerical value indicating the occurrence rate such as “25%” or “18%” is set as the occurrence frequency. This occurrence frequency is updated regularly or irregularly based on, for example, statistical information collected by a medical organization or the like. Information indicating whether or not the brain tumor is malignant is set as the property. For example, “malignant” or “benign” is set as the property.

  The example shown in FIG. 2 shows that, for example, glioma, malignant lymphoma, germ cell tumor, medulloblastoma, etc. occur in the center last time, and germ cell tumor, pituitary adenoma, etc. occur in the primary motor area. It is shown that. In addition, for example, regarding the tumor that occurs in the previous center, the frequency of glioma is 25%, and the frequency of malignant lymphoma is 3%. In addition, for example, regarding a tumor that occurs in the previous center, it is indicated that the glioma is malignant and the meningioma is benign.

  Returning to the description of FIG. 1, the functional image table storage unit 13 c stores a functional image table in which the properties of the brain tumor are associated with the types of functional images determined according to the properties of the brain tumor. FIG. 3 is a diagram illustrating an example of a functional image table stored in the functional image table storage unit 13c. As illustrated in FIG. 3, for example, the functional image table holds data in which properties, symptoms, and functional images are associated with each other for each property and symptom.

  In the property, information indicating whether or not the brain tumor is malignant is set similarly to the property set in the tumor property table. In the symptom, information indicating a local symptom generated in the subject due to the brain tumor is set. For example, “language”, “exercise”, “sight”, “none”, and the like are set as symptoms. Here, “language” indicates that a language failure has occurred. In addition, “movement” indicates a movement disorder, and “sight” indicates that a visual disturbance has occurred. “None” indicates that no brain tumor damage has occurred.

  In the function image, information indicating the type of the function image is set. For example, “DTI”, “fMRI”, “MEG”, “EEG”, etc. are set in the functional image. Here, “DTI” indicates a diffusion tensor image (DTI), and “fMRI” indicates a brain function activation image (hereinafter referred to as an fMRI image) obtained by fMRI. “MEG” indicates a magnetoencephalogram (MagnetoEncephaloGram: MEG), and “EEG” indicates an electroencephalogram (ElectroEncephaloGraphy: EEG).

  Note that the type of functional image set for such a functional image is determined in advance according to the nature and symptoms of the brain tumor. For example, a malignant brain tumor is generally often present in the brain parenchyma, but when a brain tumor is present in the brain parenchyma, infiltration occurs and it is difficult to separate the tumor from the normal tissue. Therefore, when the tumor is malignant and there is a symptom due to a brain tumor, for example, an fMRI image that images the localization of a brain function activation region and a DIT that depicts nerve fibers in the brain are displayed in the function image, respectively. Is set. When the brain tumor is malignant and there is no symptom due to the brain tumor, for example, only the fMRI image is set as the functional image.

  On the other hand, benign brain tumors often exist outside the brain parenchyma. Therefore, for a benign brain tumor, the tumor and normal tissue can be easily distinguished. Therefore, for example, when the brain tumor is benign and there is a symptom due to the brain tumor, for example, an fMRI image and an MEG are set as the functional image. When the brain tumor is benign and there is no symptom due to the brain tumor, for example, an fMRI image and an EEG are set as the functional image.

Returning to the description of FIG. 1, the composite image storage unit 13d stores a composite image of a morphological image and a functional image combined by an image display control unit 14h described later. For example, combined-image storing unit 13d stores the T ₂ weighted images in the form image of the brain, the synthesized image fMRI image and MEG are synthesized is a functional image of the brain.

  The extraction rate storage unit 13e stores a tumor extraction rate calculated by an extraction rate calculation unit 14k described later. The extraction rate stored in the extraction rate storage unit 13e is referred to, for example, after the operation, and is used as an index value for evaluating the operation.

  The control unit 14 controls the entire medical image processing apparatus 10 by executing various processes. The control unit 14 includes, for example, a processor such as a CPU (Central Processing Unit) and an MPU (Micro Processing Unit).

  As shown in FIG. 1, the control unit 14 includes a morphological image generation unit 14a, a tumor position specification unit 14b, a tumor property determination unit 14c, a functional image selection unit 14d, a functional image generation unit 14e, and an analysis image. And a generation unit 14f. In addition, the control unit 14 includes an image alignment unit 14g, an image display control unit 14h, a tumor size calculation unit 14i, an extracted portion calculation unit 14j, and an extraction rate calculation unit 14k.

The morphological image generation unit 14a generates a morphological image of the brain. For example, the morphological image generation unit 14a generates a T ₂ weighted image from the magnetic resonance data collected by an MRI apparatus (not shown) as the morphological image. Then, the morphological image generation unit 14a sends the generated morphological image to a tumor position specifying unit 14b and an image alignment unit 14g described later.

  The tumor position specifying unit 14b specifies the position of the brain tumor based on the brain morphological image generated by the morphological image generating unit 14a. For example, the tumor position specifying unit 14b extracts a brain tumor region from the morphological image sent from the morphological image generation unit 14a. Here, as a method for extracting a brain tumor region, various generally known region extraction methods can be used. Further, the tumor position specifying unit 14b refers to the brain map stored by the brain function map storage unit 13a and specifies the brain function area where the brain tumor region extracted from the morphological image is located. And the tumor position specific | specification part 14b sends the specified brain function area | region to the tumor property determination part 14c mentioned later as information which shows the position of a brain tumor.

  The tumor property determination unit 14c determines whether or not the brain tumor is malignant based on the position of the brain tumor specified by the tumor position specifying unit 14b. For example, the tumor property determination unit 14c refers to the tumor property table stored in the tumor property table storage unit 13b, and acquires information on all brain tumors corresponding to the brain function areas sent from the tumor position specifying unit 14b. . Thereafter, the tumor property determination unit 14c compares the acquired occurrence frequencies of the respective brain tumors to identify the property of the brain tumor having the highest occurrence frequency. Then, the tumor property determination unit 14c sends the specified brain tumor property to the function image selection unit 14d described later as a determination result. In addition, when the property of the specified brain tumor is malignant, the tumor property determination unit 14c instructs the analysis image generation unit 14f described later to generate a DTI.

  The functional image selection unit 14d selects the type of functional image of the brain based on the determination result of the tumor property determination unit 14c. For example, the functional image selection unit 14 d receives input of information related to the subject's symptoms from the operator via the input unit 11. Thereafter, the functional image selection unit 14d refers to the functional image table storage unit 13c, and determines the type of functional image corresponding to the determination result of the brain tumor property sent from the tumor property determination unit 14c and the symptom received from the operator. get. Then, the functional image selection unit 14d sends information indicating the type of the acquired functional image to the functional image generation unit 14e described later.

  The functional image generation unit 14e generates the type of functional image selected by the functional image selection unit 14d. For example, when information indicating the type of functional image is sent from the functional image selection unit 14d, the functional image generation unit 14e generates a functional image of that type. More specifically, for example, the functional image generation unit 14e generates an fMRI image, MEG, EEG, and the like. Then, the function image generation unit 14e sends the generated function image to the image alignment unit 14g described later.

  The analysis image generation unit 14f generates a diffusion tensor image of the brain when the determination result of the tumor property determination unit 14c is malignant. For example, when the analysis image generation unit 14f is instructed to generate a DTI from the tumor property determination unit 14c, the analysis image generation unit 14f generates a DTI from magnetic resonance data collected by an MRI apparatus (not shown). Then, the functional image generation unit 14e sends the generated DTI to the image alignment unit 14g described later.

  The image alignment unit 14g aligns the brain morphological image generated by the morphological image generation unit 14a and the functional image generated by the functional image generation unit 14e. The image alignment unit 14g further aligns the DTI generated by the analysis image generation unit 14f with the morphological image and the function image. For example, when the morphological image is sent from the morphological image generation unit 14a and the functional image is sent from the functional image generation unit 14e, the image registration unit 14g aligns the sent morphological image and functional image. In addition, when the DTI is sent from the analysis image generation unit 14f, the image alignment unit 14g further aligns the sent DTI with the morphological image and the functional image. Here, as a method of aligning the images, various generally known image alignment methods can be used. Then, the image alignment unit 14g sends the aligned images to an image display control unit 14h described later.

  The image display control unit 14h synthesizes the morphological image and the function image aligned by the image alignment unit 14g and displays them on the display unit 12. Further, the image display control unit 14 h further synthesizes the DTI generated by the analysis image generation unit 14 f with the morphological image and the functional image and causes the display unit 12 to display the combined image.

For example, when the morphological image, the functional image, and the DIT are sent from the image alignment unit 14g, the image display control unit 14h generates a combined image by combining the images. Then, the image display control unit 14h causes the display unit 12 to display the generated composite image. FIG. 4 is a diagram illustrating an example of a composite image displayed by the image display control unit 14h. The upper left of FIG. 4, the upper right and lower right of the image is T ₂ weighted images of the head captured by the MRI apparatus, respectively. The lower left image is a rendering image in which the head of the subject is depicted three-dimensionally.

As shown in the lower right image in FIG. 4, for example, the image display control unit 14 h generates a composite image in which the fMRI image, the DTI, and the MEG are superimposed on the T ₂ weighted image, and the generated composite image is displayed. It is displayed on the display unit 12. As a result, on the synthesized image, the brain tumor T depicted by the T ₂ weighted image, the brain function activation region B depicted by the fMRI image, the nerve bundle N depicted by the DTI, and the result M of the magnetoencephalogram depicted by the MEG Is shown respectively.

  The image display control unit 14h may change the display mode of the tumor surface according to the shape of the brain tumor T when displaying the composite image. More specifically, for example, the image display control unit 14h changes the concentration of the tumor surface or changes the color according to the shape of the brain tumor T.

  The tumor size calculation unit 14i calculates the size of the brain tumor based on the brain morphological image generated by the morphological image generation unit 14a. For example, after the composite image is displayed by the image display control unit 14h, the tumor size calculation unit 14i acquires the morphological image combined with the composite image from the morphological image generation unit 14a, and from the acquired morphological image. Extract brain tumor area. Here, as a method for extracting a brain tumor region, various generally known region extraction methods can be used. Further, the tumor size calculation unit 14i calculates the size of the extracted brain tumor. Then, the tumor size calculation unit 14i sends the calculated brain tumor size to the extraction rate calculation unit 14k described later.

  The extracted part calculation unit 14j calculates the size of the extracted part extracted from the brain tumor. For example, for the brain tumor on the composite image displayed on the display unit 12 via the input unit 11, the extracted part calculation unit 14 j performs an operation to specify the range of the extracted part that is extracted from the brain tumor at the time of surgery. Accept from. Thereafter, the extracted portion calculation unit 14j calculates the size of the extracted portion based on the range specified on the composite image by the operator. And the extraction part calculation part 14j sends the magnitude | size of the extraction part calculated to the extraction rate calculation part 14k mentioned later.

  The extraction rate calculation unit 14k calculates a tumor extraction rate from the size of the brain tumor calculated by the tumor size calculation unit 14i and the size of the extraction part calculated by the extraction part calculation unit 14j. For example, when the size of the brain tumor is sent from the tumor size calculation unit 14i and the size of the removal part is sent from the extraction part calculation unit 14j, the extraction rate calculation unit 14k is expressed by the following equation (1). The removal rate of the brain tumor is calculated. Then, the extraction rate calculation unit 14k stores the calculated extraction rate in the extraction rate storage unit 13e.

    Extraction rate [%] = (size of extracted part / size of brain tumor) × 100 (1)

  As described above, the removal rate calculation unit 14k calculates the removal rate of the tumor, so that, for example, the operation can be evaluated after the operation using the removal rate as an index value.

  Next, a processing procedure of processing executed by the medical image processing apparatus 100 according to the present embodiment will be described. FIG. 5 is a flowchart illustrating a processing procedure of processing executed by the medical image processing apparatus 100 according to the present embodiment. As illustrated in FIG. 5, in the medical image processing apparatus 100, for example, when the control unit 14 receives an instruction to start processing from the operator via the input unit 11 (Yes in step S <b> 101), the following processing is performed. Execute.

  First, the morphological image generation unit 14a generates a morphological image of the brain (step S102). Subsequently, the tumor position specifying unit 14b specifies the position of the brain tumor based on the brain morphological image generated by the morphological image generating unit 14a (step S103). Then, the tumor property determining unit 14c determines whether or not the brain tumor is malignant based on the position of the brain tumor specified by the tumor position specifying unit 14b (step S104).

  Subsequently, the functional image selection unit 14d selects the type of functional image based on the determination result of the tumor property determination unit 14c (step S105). Thereafter, the function image generation unit 14e generates a function image of the type selected by the function image selection unit 14d (step S106). Also, as a result of the determination by the tumor property determination unit 14c, if the brain tumor is malignant (step S107, Yes), the analysis image generation unit 14f generates a DTI (step S108).

  Subsequently, the image alignment unit 14g aligns each generated image (functional image and / or DTI) with the morphological image (step S109). Then, the image display control unit 14h synthesizes the images aligned by the image alignment unit 14g and displays them on the display unit 12 (step S110). In addition, the image display control unit 14h stores the composite image displayed by the image display control unit 14h in the composite image storage unit 13d (Step S111).

  Subsequently, the tumor size calculator 14i calculates the size of the brain tumor based on the brain morphological image generated by the morphological image generator 14a (step S112). In addition, the extracted part calculation unit 14j calculates the size of the extracted part based on the range specified on the composite image by the operator (step S113).

  Then, the extraction rate calculation unit 14k calculates the tumor extraction rate from the size of the brain tumor calculated by the tumor size calculation unit 14i and the size of the extraction part calculated by the extraction part calculation unit 14j (step S114). . Also, the extraction rate calculation unit 14k stores the calculated extraction rate in the extraction rate storage unit 13e (step S115).

  As described above, in the present embodiment, the tumor position specifying unit 14b specifies the position of the brain tumor based on the brain morphological image generated by the morphological image generating unit 14a. The tumor property determination unit 14c determines whether the brain tumor is malignant based on the position of the brain tumor specified by the tumor position specifying unit 14b. In addition, the functional image selection unit 14d selects the type of functional image of the brain based on the determination result of the tumor property determination unit 14c. Then, the function image generation unit 14e generates the type of function image selected by the function image selection unit 14d. That is, in this embodiment, the type of functional image suitable for studying the surgical plan is automatically selected. Therefore, according to the present embodiment, it is possible to assist the operator so that an operation plan for brain tumor can be easily planned. In addition, it becomes possible to plan a preoperative surgical plan efficiently in a shorter period of time.

  In the present embodiment, the image alignment unit 14g aligns the brain morphological image generated by the morphological image generation unit 14a and the functional image generated by the functional image generation unit 14e. Then, the image display control unit 14h synthesizes the morphological image and the functional image aligned by the image alignment unit 14g and displays them on the display unit 12. Therefore, according to the present embodiment, it is possible to more accurately identify the positional relationship between the functional area of the brain and the tumor, so that it is possible to make a surgical plan for the brain tumor more accurately. This makes it possible to improve treatment efficiency and reduce intraoperative risk.

  In this embodiment, the analysis image generation unit 14f generates a DTI of the brain when the determination result of the tumor property determination unit 14c is malignant. Then, the image alignment unit 14g further aligns the DTI generated by the analysis image generation unit 14f with the morphological image and the functional image. Then, the image display control unit 14h further synthesizes the DTI generated by the analysis image generation unit 14f with the morphological image and the functional image and causes the display unit 12 to display the combined image. Therefore, according to the present embodiment, the operator can more easily grasp the positional relationship between the brain tumor and the surrounding brain functional areas with reference to the nerve bundle drawn by the DTI.

  In this embodiment, the image display control unit 14h changes the display mode of the tumor surface for the brain tumor depicted in the morphological image according to the shape of the brain tumor. Therefore, according to the present embodiment, the operator can easily recognize the shape of the tumor.

  In this embodiment, the tumor size calculator 14i calculates the size of the brain tumor based on the brain morphological image generated by the morphological image generator 14a. In addition, the extracted part calculation unit 14j calculates the size of the extracted part extracted from the brain tumor. Then, the extraction rate calculation unit 14k calculates the tumor extraction rate from the size of the brain tumor calculated by the tumor size calculation unit 14i and the size of the extraction part calculated by the extraction part calculation unit 14j. Therefore, according to the present embodiment, for example, an operation performed after surgery by using an extraction rate calculated before surgery as an index value and comparing the index value with an extraction rate calculated after surgery. It becomes possible to evaluate appropriately.

In the present embodiment, T ₂ weighted images and fMRI image, such as DTI, a case has been described in which an image obtained by the MRI apparatus is used, the present invention is not limited thereto. For example, the present invention can be similarly applied even when an image obtained by another medical image diagnostic apparatus such as an X-ray CT (Computed Tomography) apparatus, a PET (Positron Emission Tomography) apparatus, or an ultrasonic diagnostic apparatus is used. It is.

DESCRIPTION OF SYMBOLS 10 Medical image processing apparatus 14 Control part 14b Tumor position specific | specification part 14c Tumor property determination part 14d Functional image selection part 14e Functional image generation part

Claims (5)

A tumor location specifying means for specifying the location of a brain tumor based on a brain morphological image;
A tumor property determining means for determining the property of the brain tumor based on the position of the brain tumor specified by the tumor position specifying means;
Functional image selection means for selecting the type of functional image of the brain based on the determination result of the tumor property determination means;
A medical image processing apparatus comprising: functional image generation means for generating a functional image of the type selected by the functional image selection means. Image alignment means for aligning the morphological image and the functional image generated by the functional image generation means;
The medical image processing apparatus according to claim 1, further comprising: an image display control unit that synthesizes the morphological image and the functional image aligned by the image alignment unit and displays the combined image on a display unit. An image generating means for generating a diffusion tensor image of the brain when the determination result of the tumor property determining means is malignant;
The image alignment means further aligns the diffusion tensor image generated by the image generation means with the morphological image and the functional image,
The medical image according to claim 2, wherein the image display control unit further synthesizes the diffusion tensor image generated by the image generation unit with the morphological image and the functional image and displays the combined image on the display unit. Processing equipment. The medical image processing apparatus according to claim 2, wherein the image display control unit changes a display mode of a brain tumor region according to a shape of the brain tumor for the brain tumor depicted in the morphological image. A tumor size calculating means for calculating the size of the brain tumor based on the morphological image;
An extracted portion calculating means for calculating the size of the extracted portion extracted from the brain tumor;
An extraction rate calculating means for calculating a tumor removal rate from the size of the brain tumor calculated by the tumor size calculating means and the size of the extracted part calculated by the extracted portion calculating means; The medical image processing apparatus according to any one of claims 1 to 4.