Unit-Based Histopathology Tissue Segmentation via Multi-Level Feature Representation

• URL: http://arxiv.org/abs/2507.12427v1
• Date: Wed, 16 Jul 2025 17:15:18 GMT
• Title: Unit-Based Histopathology Tissue Segmentation via Multi-Level Feature Representation
• Authors: Ashkan Shakarami, Azade Farshad, Yousef Yeganeh, Lorenzo Nicole, Peter Schuffler, Stefano Ghidoni, Nassir Navab,
• Abstract summary: UTS is a unit-based tissue segmentation framework for histopathology.<n>It classifies each fixed-size 32 * 32 tile, rather than each pixel, as the segmentation unit.<n>It supports clinically relevant tasks such as tumor-stroma quantification and surgical margin assessment.
• Score: 36.53156355717765
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose UTS, a unit-based tissue segmentation framework for histopathology that classifies each fixed-size 32 * 32 tile, rather than each pixel, as the segmentation unit. This approach reduces annotation effort and improves computational efficiency without compromising accuracy. To implement this approach, we introduce a Multi-Level Vision Transformer (L-ViT), which benefits the multi-level feature representation to capture both fine-grained morphology and global tissue context. Trained to segment breast tissue into three categories (infiltrating tumor, non-neoplastic stroma, and fat), UTS supports clinically relevant tasks such as tumor-stroma quantification and surgical margin assessment. Evaluated on 386,371 tiles from 459 H&E-stained regions, it outperforms U-Net variants and transformer-based baselines. Code and Dataset will be available at GitHub.

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