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

TL;DR

VISTA-PATH is an interactive, class-aware foundation model for pathology image segmentation that incorporates expert feedback, supports dynamic refinement, and improves clinical interpretability and tissue analysis.

Contribution

It introduces VISTA-PATH, a novel interactive foundation model for pathology segmentation that integrates visual context, semantic descriptions, and expert prompts, supported by a large-scale curated dataset.

Findings

VISTA-PATH outperforms existing models on multiple benchmarks.

Supports human-in-the-loop refinement with sparse annotations.

Enhances tissue microenvironment analysis with a new Tumor Interaction Score.

Abstract

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.