GloW-VSNet: グローバルビュー白斑病変セグメンテーションのためのスクラッチベースの弱教師ありフレームワーク

Vitiligo lesion identification is essential for quantifying disease severity, monitoring disease progression and assessing treatment response, particularly for objective quantification. However, segmenting vitiligo lesions from clinical images is challenging due to indistinct borders, complex backgrounds, and image artifacts. The difficulty increases when handling small and sparse lesions in global-view photographs. Fully supervised segmentation models require extensively annotated datasets, making the labelling process time-consuming and costly. To address these challenges, we propose GloW-VSNet, a scribble-guided weakly supervised segmentation method for global-view vitiligo detection. Our approach integrates differentiable feature clustering with a spatial attention mechanism based on physician-provided scribble annotations, enabling the model to focus on relevant spatial features and improve segmentation accuracy despite background noise and artifacts. Additionally, we introduce spatial continuity optimization to preserve the natural distribution of vitiligo, enhancing segmentation consistency while reducing computational demands. Extensive experiments on two public vitiligo datasets and two private datasets demonstrate that GloW-VSNet achieves state-of-the-art performance. To our knowledge, this is the first study to explore weakly supervised global-view vitiligo segmentation, addressing a critical research gap. Our method enhances the assessment of disease severity and monitoring of treatment response through an objective assessment for real-world applications. Our code is publicly available at https://github.com/YuhanZheng0327/Weakly-Supervised-Vitiligo-Lesion-Segmentation.