Diabetic foot ulcers pose health risks, including higher morbidity, mortality, and amputation rates. Monitoring wound areas is crucial for proper care, but manual segmentation is subjective due to complex wound features and background variation. Expert annotations are costly and time-intensive, thus hampering large dataset creation. Existing segmentation models relying on extensive annotations are impractical in real-world scenarios with limited annotated data. In this paper, we propose a cross-domain augmentation method named TransMix that combines Augmented Global Pre-training AGP and Localized CutMix Fine-tuning LCF to enrich wound segmentation data for model learning. TransMix can effectively improve the foot ulcer segmentation model training by leveraging other dermatology datasets not on ulcer skins or wounds. AGP effectively increases the overall image variability, while LCF increases the diversity of wound regions. Experimental results show that TransMix increases the variability of wound regions and substantially improves the Dice score for models trained with only 40 annotated images under various proportions.

翻译：糖尿病足溃疡对健康构成风险，包括较高的发病率、死亡率和截肢率。监测伤口区域对于适当护理至关重要，但由于复杂的伤口特征和背景变化，人工分割具有主观性。专家标注成本高且耗时，阻碍了大规模数据集的创建。依赖大量标注的现有分割模型在标注数据有限的实际场景中不实用。本文提出一种名为TransMix的跨域增强方法，结合增强全局预训练（AGP）和局部CutMix微调（LCF）来丰富伤口分割数据以支持模型学习。TransMix通过利用非溃疡皮肤或伤口的其他皮肤病数据集，能有效改善足部溃疡分割模型训练。AGP有效增加整体图像变异性，而LCF则增强伤口区域的多样性。实验结果表明，TransMix能提高伤口区域变异性，并在仅使用40张不同比例标注图像训练的模型下显著提升Dice分数。