Semantic Segmentation of Diabetic Foot Ulcer Images: Dealing with Small Dataset in DL Approaches

Rania, Niri; Douzi, Hassan; Yves, Lucas; Sylvie, Treuillet

Foot ulceration is the most common complication of diabetes and represents a major health problem all over the world. If these ulcers are not adequately treated in an early stage, they may lead to lower limb amputation. Considering the low-cost and prevalence of smartphones with a high-resolution camera, Diabetic Foot Ulcer (DFU) healing assessment by image analysis became an attractive option to help clinicians for a more accurate and objective management of the ulcer. In this work, we performed DFU segmentation using Deep Learning methods for semantic segmentation. Our aim was to find an accurate fully convolutional neural network suitable to our small database. Three different fully convolutional networks have been tested to perform the ulcer area segmentation. The U-Net network obtained a Dice Similarity Coefficient of 97.25% and an intersection over union index of 94.86%. These preliminary results demonstrate the power of fully convolutional neural networks in diabetic foot ulcer segmentation using a limited number of training samples.