LUTSeg：用于溃疡组织分割的纵向多专家数据集
LUTSeg: A Longitudinal Multi-Expert Dataset for Ulcer Tissue Segmentation
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中文总结 AI 辅助
研究针对慢性溃疡组织分割数据稀缺问题，构建LUTSeg数据集，并提出半监督框架TiSage，在低标注设置下优于基线方法。
中文摘要 AI 辅助
量化伤口组织组成对于监测慢性溃疡进展和指导治疗决策至关重要，但像素级标注成本高昂，且多组织伤口数据集仍然稀缺，尤其是针对麻风病等被忽视疾病的数据集。我们推出LUTSeg，这是一个包含39名患者141张图像的纵向慢性溃疡数据集，配有伤口掩码和由5名专家临床医生标注的5种组织类别，其中包含用于评分者间一致性分析的多专家金标准子集。为建立LUTSeg的初始基准，我们进一步提出TiSage，一种半监督组织分割框架，在教师-学生架构中整合来自冻结医学视觉-语言模型的多尺度语义先验。我们在LUTSeg和DFUTissue上评估TiSage，结果显示其在大多数低标注设置下优于监督和半监督基线。代码与数据：this https URL
英文摘要
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
发表机构
- King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）
- Subred Norte E.S.E, Hospital Simón Bolívar（北红区E.S.E西蒙·玻利瓦尔医院）
- Universidad del Rosario（罗萨里奥大学）
机构由 AI 辅助整理，请以论文原文为准。
