Fully Convolutional Networks for Diabetic Foot Ulcer Segmentation
This addresses the high-cost and lengthy care challenges in DFU treatment for diabetic patients, though it is incremental as it applies existing FCN methods with transfer learning to a new medical dataset.
The paper tackles the problem of segmenting Diabetic Foot Ulcers (DFU) and surrounding skin from foot images, achieving Dice Similarity Coefficients of 0.794 for ulcer, 0.851 for skin, and 0.899 for combined regions using a two-tier transfer learning approach with Fully Convolutional Networks.
Diabetic Foot Ulcer (DFU) is a major complication of Diabetes, which if not managed properly can lead to amputation. DFU can appear anywhere on the foot and can vary in size, colour, and contrast depending on various pathologies. Current clinical approaches to DFU treatment rely on patients and clinician vigilance, which has significant limitations such as the high cost involved in the diagnosis, treatment and lengthy care of the DFU. We introduce a dataset of 705 foot images. We provide the ground truth of ulcer region and the surrounding skin that is an important indicator for clinicians to assess the progress of ulcer. Then, we propose a two-tier transfer learning from bigger datasets to train the Fully Convolutional Networks (FCNs) to automatically segment the ulcer and surrounding skin. Using 5-fold cross-validation, the proposed two-tier transfer learning FCN Models achieve a Dice Similarity Coefficient of 0.794 ($\pm$0.104) for ulcer region, 0.851 ($\pm$0.148) for surrounding skin region, and 0.899 ($\pm$0.072) for the combination of both regions. This demonstrates the potential of FCNs in DFU segmentation, which can be further improved with a larger dataset.