A Superpixel-Wise Fully Convolutional Neural Network Approach for Diabetic Foot Ulcer Tissue Classification

Abstract

Accurate assessment of diabetic foot ulcers (DFU) is primordial to provide an efficient treatment and to prevent amputation. Traditional DFU assessment methods used by clinicians are based on visual examination of the ulcer by estimating the surface and analyzing tissue conditions. These manual methods are subjective and make direct contact with the wound, resulting in high variability and risk of infection. In this research work, we propose a novel smartphone-based skin telemonitoring system to support medical diagnoses and decisions during DFU tissues examination. The database contains 219 images, for effective tissue identification and annotation of the ground truth, a graphical interface based on superpixel segmentation method has been used. Our method performs DFU assessment in an end-to-end style comprising automatic ulcer segmentation and tissue classification. The classification task is performed at a patch-level, superpixels extracted with SLIC are used as input for the training of the deep neural network. State-of-the-art deep learning models for semantic segmentation have been used to perform tissue differentiation within the ulcer area into three classes (Necrosis, Granulation and Slough) and have been compared to the proposed method. The proposed superpixel-based method outperforms classic fully convolutional network models while improving significantly the performance on all the metrics. Accuracy and DICE index are improved from 84.55% to 92.68% and from 54.31% to 75.74% respectively for FCN-32. The results reveal robust tissue classification effectiveness and the potential of our system to monitor DFU healing over time.