Diabetic Wound Segmentation using Convolutional Neural Networks

Image segmentation is a common goal in many medical applications, as its use can improve diagnostic capability and outcome prediction. In order to assess the wound healing rate in diabetic foot ulcers, some parameters from the wound area are measured. However, heterogeneity of diabetic skin lesions and the noise present in images captured by digital cameras make wound extraction a difficult task. In this work, a Deep Learning based method for accurate segmentation of wound regions is proposed. In the proposed method, input images are first processed to remove artifacts and then fed into a Convolutional Neural Network (CNN), producing a probability map. Finally, the probability maps are processed to extract the wound region. We also address the problem of removing some false positives. Experiments show that our method can achieve high performance in terms of segmentation accuracy and Dice index.