Fully Automatic Wound Segmentation with Deep Convolutional Neural Networks
This work addresses the need for accurate wound area measurement in healthcare to improve diagnosis and treatment, particularly for foot ulcers, but it is incremental as it builds on existing deep learning methods.
The paper tackled the problem of fully automatic wound segmentation in natural images to aid diagnosis and care, achieving effective results with a novel convolutional framework based on MobileNetV2 and connected component labeling, as demonstrated through comprehensive experiments on a dataset of 1,109 foot ulcer images.
Acute and chronic wounds have varying etiologies and are an economic burden to healthcare systems around the world. The advanced wound care market is expected to exceed $22 billion by 2024. Wound care professionals rely heavily on images and image documentation for proper diagnosis and treatment. Unfortunately lack of expertise can lead to improper diagnosis of wound etiology and inaccurate wound management and documentation. Fully automatic segmentation of wound areas in natural images is an important part of the diagnosis and care protocol since it is crucial to measure the area of the wound and provide quantitative parameters in the treatment. Various deep learning models have gained success in image analysis including semantic segmentation. Particularly, MobileNetV2 stands out among others due to its lightweight architecture and uncompromised performance. This manuscript proposes a novel convolutional framework based on MobileNetV2 and connected component labelling to segment wound regions from natural images. We build an annotated wound image dataset consisting of 1,109 foot ulcer images from 889 patients to train and test the deep learning models. We demonstrate the effectiveness and mobility of our method by conducting comprehensive experiments and analyses on various segmentation neural networks.