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

• URL: http://arxiv.org/abs/2608.25866v1
• Date: Wed, 26 Aug 2026 14:39:30 GMT
• Title: LUTSeg: A Longitudinal Multi-Expert Dataset for Ulcer Tissue Segmentation
• Abstract summary: Quantifying wound tissue composition is essential for monitoring chronic ulcer progression and guiding treatment decisions.<n>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.<n>We 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.
• Score: 37.740722264328205
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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

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