Improving Limited Supervised Foot Ulcer Segmentation Using Cross-Domain
  Augmentation
Shang-Jui Kuo Po-Han Huang Chia-Ching Lin Jeng-Lin Li Ming-Ching Chang
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.