Automated segmentation of diabetic foot ulcers (DFUs) plays a critical role in clinical diagnosis, therapeutic planning, and longitudinal wound monitoring. However, this task remains challenging due to the heterogeneous appearance, irregular morphology, and complex backgrounds associated with ulcer regions in clinical photographs. Traditional convolutional neural networks (CNNs), such as U-Net, provide strong localization capabilities but struggle to model long-range spatial dependencies due to their inherently limited receptive fields. To address this, we employ the TransUNet architecture, a hybrid framework that integrates the global attention mechanism of Vision Transformers (ViTs) into the U-Net structure. This combination allows the model to extract global contextual features while maintaining fine-grained spatial resolution. We trained the model on the public Foot Ulcer Segmentation Challenge (FUSeg) dataset using a robust augmentation pipeline and a hybrid loss function to mitigate class imbalance. On the validation set, the model achieved a Dice Similarity Coefficient (F1-score) of 0.8799 using an optimized threshold of 0.4389. To ensure clinical transparency, we integrated Grad-CAM visualizations to highlight model focus areas. Furthermore, a clinical utility analysis demonstrated a strong correlation (Pearson r = 0.9631) between predicted and ground-truth wound areas. These outcomes demonstrate that our approach effectively integrates global and local feature extraction, offering a reliable, effective, and explainable solution for automated foot ulcer assessment.

翻译：糖尿病足溃疡（DFUs）的自动分割在临床诊断、治疗规划及伤口纵向监测中具有关键作用。然而，由于临床照片中溃疡区域外观异质性、形态不规则及背景复杂，该任务仍具挑战性。传统卷积神经网络（CNNs）如U-Net虽具备较强的定位能力，但受限于固有感受野，难以建模长距离空间依赖关系。为此，我们采用TransUNet架构——一种将视觉Transformer（ViTs）的全局注意力机制融入U-Net结构的混合框架。该组合使模型能在保持细粒度空间分辨率的同时提取全局上下文特征。我们在公开的足部溃疡分割挑战赛（FUSeg）数据集上训练模型，采用鲁棒的数据增强流程和混合损失函数以缓解类别不平衡问题。在验证集上，模型通过0.4389的优化阈值实现了0.8799的Dice相似系数（F1分数）。为确保临床可解释性，我们集成Grad-CAM可视化以突出模型关注区域。此外，临床效用分析显示预测伤口面积与真实标注面积间存在强相关性（Pearson r = 0.9631）。这些结果表明，我们的方法有效融合了全局与局部特征提取，为自动化足部溃疡评估提供了可靠、高效且可解释的解决方案。