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Paper page - VISTA-PATH: An interactive foundation model for pathology image segmentation and quantitative analysis in computational pathology
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arxiv:2601.16451

VISTA-PATH: An interactive foundation model for pathology image segmentation and quantitative analysis in computational pathology

Published on Jan 23
Ā· Submitted by
Zhi Huang
on Jan 26
Authors:
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Abstract

VISTA-PATH is an interactive, class-aware pathology segmentation model that integrates visual context, semantic descriptions, and expert feedback to enable precise multi-class segmentation and clinical interpretation in digital pathology.

Accurate semantic segmentation for histopathology image is crucial for quantitative tissue analysis and downstream clinical modeling. Recent segmentation foundation models have improved generalization through large-scale pretraining, yet remain poorly aligned with pathology because they treat segmentation as a static visual prediction task. Here we present VISTA-PATH, an interactive, class-aware pathology segmentation foundation model designed to resolve heterogeneous structures, incorporate expert feedback, and produce pixel-level segmentation that are directly meaningful for clinical interpretation. VISTA-PATH jointly conditions segmentation on visual context, semantic tissue descriptions, and optional expert-provided spatial prompts, enabling precise multi-class segmentation across heterogeneous pathology images. To support this paradigm, we curate VISTA-PATH Data, a large-scale pathology segmentation corpus comprising over 1.6 million image-mask-text triplets spanning 9 organs and 93 tissue classes. Across extensive held-out and external benchmarks, VISTA-PATH consistently outperforms existing segmentation foundation models. Importantly, VISTA-PATH supports dynamic human-in-the-loop refinement by propagating sparse, patch-level bounding-box annotation feedback into whole-slide segmentation. Finally, we show that the high-fidelity, class-aware segmentation produced by VISTA-PATH is a preferred model for computational pathology. It improve tissue microenvironment analysis through proposed Tumor Interaction Score (TIS), which exhibits strong and significant associations with patient survival. Together, these results establish VISTA-PATH as a foundation model that elevates pathology image segmentation from a static prediction to an interactive and clinically grounded representation for digital pathology. Source code and demo can be found at https://github.com/zhihuanglab/VISTA-PATH.

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šŸš€ VISTA-PATH is introduced as the first interactive segmentation foundation model for pathology.
It advances computational pathology workflows by enabling more accurate, interpretable, and human-guided quantitative measurements. Key highlights include:
1ļøāƒ£ Large-scale training: Trained on over 1.6M image-mask-text pairs
2ļøāƒ£ State-of-the-art performance: Accurate segmentation across both in-distribution and out-of-distribution tissues
3ļøāƒ£ Interactive refinement: Outputs can be efficiently refined using bounding-box prompts with only a few active-learning steps
4ļøāƒ£ Deployment-ready: Fully integrated into https://tissuelab.org and available for immediate use
šŸ“„ Read the arXiv preprint: https://arxiv.org/abs/2601.16451

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