Improving Limited Supervised Foot Ulcer Segmentation Using Cross-Domain Augmentation
This work addresses the challenge of costly expert annotations for wound monitoring in healthcare, though it is incremental as it builds on existing augmentation techniques.
The paper tackled the problem of limited annotated data for diabetic foot ulcer segmentation by proposing a cross-domain augmentation method called TransMix, which improved the Dice score for models trained with only 40 annotated images.
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.