LUTSeg: A Longitudinal Multi-Expert Dataset for Ulcer Tissue Segmentation

2026-08-26

AI Summary

为了解决慢性溃疡组织分割数据稀缺问题，通过构建LUTSeg数据集，并提出TiSage半监督分割框架来提高分割精度。

Abstract

Quantifying wound tissue composition is essential for monitoring chronic ulcer progression and guiding treatment decisions. However, pixel-level annotations are costly, and multi-tissue wound datasets remain scarce, particularly for neglected diseases such as leprosy. We introduce LUTSeg, a longitudinal chronic ulcer dataset comprising 141 images from 39 patients with wound masks and five tissue categories annotated by five expert clinicians, including a multi-expert gold-standard subset for inter-rater agreement analysis. To establish an initial benchmark for LUTSeg, we further propose TiSage, a semi-supervised tissue segmentation framework that integrates multi-scale semantic priors from a frozen medical vision-language model within a teacher-student architecture. We evaluate TiSage on LUTSeg and DFUTissue, showing improvements over supervised and semi-supervised baselines in most low-label settings. Code & data: https://github.com/carlosh93/TiSage

Problem

• ulcer tissue segmentation
• pixel-level annotations
• chronic ulcer
• multi-tissue wound datasets
• leprosy

Innovation

• LUTSeg
• semi-supervised segmentation
• multi-scale semantic priors
• teacher-student architecture