  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.
