构建首个麻风溃疡多专家标注数据集，助力慢性伤口组织分割研究
LUTSeg: A Longitudinal Multi-Expert Dataset for Ulcer Tissue Segmentation
- 采用多专家标注建立高质量纵向溃疡图像数据集
- 在低标注数据下，分割精度显著优于现有方法
- 适合医学图像分析、慢性伤口研究及半监督学习方向的学者
量化创面组织成分对监测慢性溃疡进展和指导治疗至关重要。然而，像素级标注成本高，且多组织创面数据集稀缺，尤其针对麻风等被忽视疾病。我们提出LUTSeg，一个纵向慢性溃疡数据集，包含39名患者的141张图像，由五位临床专家标注五类组织，并建立多专家金标准子集用于评估一致性。为建立基准，我们进一步提出TiSage，一种基于冻结医学视觉-语言模型的多尺度语义先验的半监督分割框架，采用教师-学生结构。在LUTSeg和DFUTissue上评估显示，该方法在多数低标签设置下优于监督与半监督基线。
原文摘要 · Abstract (English)
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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