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

arXiv: https://arxiv.org/abs/2608.25866 · HTML: https://mlsanity.com/paper/2608.25866/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 26 Aug 2026.

## Builds on (most cited first, 10 of 10)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [MedGemma Technical Report](https://mlsanity.com/paper/2507.05201/) (2025)
- [UniMatch V2: Pushing the Limit of Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2410.10777/) (2024)
- [Wound Tissue Segmentation in Diabetic Foot Ulcer Images Using Deep Learning: A Pilot Study](https://mlsanity.com/paper/2406.16012/) (2024)
- [ComplexWoundDB: A Database for Automatic Complex Wound Tissue Categorization](https://mlsanity.com/paper/2209.12822/) (2022)
- [CO2Wounds-V2: Extended Chronic Wounds Dataset From Leprosy Patients](https://mlsanity.com/paper/2408.10827/) (2024)
- [Deep Learning for Wound Tissue Segmentation: A Comprehensive Evaluation using A Novel Dataset](https://mlsanity.com/paper/2502.10652/) (2025)

## Abstract (arXiv)

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