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

Karen Sanchez; Carlos Hinojosa; Albert A. Ávila; Andrea C. Riano-Rojas; Diego H. Romero; Jenny C. Páez; Martina Llinás; Bernard Ghanem

cs.CVAug 26, 2026

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

Authors: Karen Sanchez, Carlos Hinojosa, Albert A. Ávila, Andrea C. Riano-Rojas, Diego H. Romero, Jenny C. Páez, Martina Llinás, Bernard Ghanem

Organizations: King Abdullah University of Science and Technology (KAUST), Saudi Arabia · Subred Norte E.S.E, Hospital Simón Bolívar, Colombia · Universidad del Rosario, Bogotá, Colombia

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

Figures & tables

Figure 1: Samples of the LUTSeg dataset. First-visit image, its binary wound mask, and pixel-level segmentation into five tissue categories for each visit image.

Figure 2: (Left) ICC(3,1) for tissue proportion agreement across annotators. (Right) Pairwise Dice scores on 46 images annotated by 5 clinicians (gold-standard subset).

Figure 3: (A) Multi-scale semantic prior construction: coarse and fine SLIC superpixels are encoded with a frozen MedSigLIP encoder, classified, and fused in log space to produce a multi-scale prior $q(x)$ . (B) EMA teacher predictions $p(x)$ from weakly augmented images. (C) Pixel-adaptive fusion of teacher and prior based on teacher confidence, yielding calibrated pseudo-labels $\tilde{p}(x)$ .

	DFUTissue								LUTSeg
	Fixed		1/4		1/8		1/16		1/4		1/8		1/16
Method	mIoU	F1	mIoU	Dice	mIoU	Dice	mIoU	Dice	mIoU	Dice	mIoU	Dice	mIoU	Dice
Supervised methods
DeepLabV3+–R50	70.02	81.12	65.22	77.14	58.32	70.52	48.38	58.87	19.89	23.17	20.79	24.18	21.25	25.34
DINOv2–DPT	68.71	80.21	66.23	78.03	64.68	76.57	52.83	65.02	28.38	35.19	20.47	23.55	24.47	30.15
Semi-supervised methods

Table 1: Comparison of supervised and semi-supervised segmentation methods. In DFUTissue, Fixed denotes the official predefined SSL split. Supervised methods use only the labeled portion of each split, whereas semi-supervised methods additionally use the corresponding unlabeled images. DeepLabV3+ uses an ImageNet-pretrained ResNet-50 encoder; DINOv2-DPT and all SSL methods use DINOv2-Base. For LUTSeg, the fully supervised references using all 111 labeled training images are

31.37/39.19

mIoU/Dice for DINOv2–DPT and

22.06/25.61

for DeepLabV3+.

Method	DFUTissue					LUTSeg
Method	Bg	Fibrin	Gran.	Callus	mIoU	Bg	Epi	Slough	Gran.	Necr.	Other	mIoU
UniMatch-V2	88.3	47.3	86.9	57.2	69.94	94.8	25.4	5.6	39.7	0.0	0.0	27.60
TiSage (Ours)	88.3	57.6	86.9	56.6	72.36	95.7	26.5	15.4	52.4	0.1	0.0	31.70

Table 2: Per-class IoU (%) results on the DFUTissue and LUTSeg for 1/8 label regime.

Prior setup	DFUTissue		LUTSeg
Prior setup	Pixel Acc (%)	mIoU (%)	Pixel Acc (%)	mIoU (%)
Zero-shot (single-scale SLIC)	76.24	34.70	67.94	16.39
Classifier (single-scale SLIC)	79.40	45.67	80.06	24.02
Classifier (coarse only)	74.71	44.70	77.48	23.38
Classifier (fine only)	81.55	48.05	81.00	25.44
Classifier (fused)	80.92	52.11	81.77	26.28

Table 3: MedSigLIP prior-only performance on DFUTissue and LUTSeg on val split.

Figure 4: Qualitative comparison of (a) Ground Truth, (b) UniMatch-V2, and (c) TiSage results for two random samples from the LUTSeg proposed dataset.

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May 19, 2026J/K move · Enter open · S save

WoundFormer: Multi-Scale Spatial Feature Fusion for Multi-Class Wound Tissue Segmentation

Muhammad Ashad Kabir, Rabin Dulal

School of Computing, Mathematics and Engineering, Charles Sturt University, NSW, Australia

Multi-Scale Feature Fusion Medical Image Segmentation

Open paper AskPDF arXiv

Chronic wounds such as diabetic foot ulcers and pressure injuries require accurate tissue-level assessment to guide treatment planning and monitor healing progression. While deep learning methods have advanced automated wound analysis, most existing approaches focus on binary segmentation and inadequately model heterogeneous tissue composition due to high intra-class variability and limited annotated data. Multi-class wound tissue segmentation, therefore, remains a challenging and clinically relevant problem. We propose WoundFormer, a transformer-based framework that enhances hierarchical spatial feature fusion for multi-class wound tissue segmentation. Specifically, we replace the standard SegFormer decoder with a spatially-preserving multi-scale aggregation head that maintains feature topology during cross-scale integration and strengthens contextual interactions through convolutional fusion. This design improves boundary localization and discrimination between visually similar tissue categories while preserving transformer efficiency. We evaluate WoundFormer on the WoundTissueSeg dataset (147 images, six tissue classes) and a second benchmark (DFUTissue dataset). The proposed method achieves an overall Dice score of 81.9%, outperforming strong CNN- and transformer-based baselines by up to 4.3 Dice points on the WoundTissueSeg benchmark, with consistent improvements across minority tissue classes. These results indicate that explicit modeling of hierarchical spatial interactions enhances transformer representations for heterogeneous wound tissue segmentation and supports more reliable quantitative wound assessment.

Abstract

Medical Image Segmentation

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