Abstract
Medical image segmentation is critical for effective computer-aided diagnosis and localization of ailments. Automated segmentation of wound regions from patient images can aid clinicians in measuring and managing chronic wounds and monitoring the wound healing trajectory. While there exists a plethora of work on general medical image segmentation, there is hardly any work on wound image analysis and segmentation. Existing methods are limited to segmenting a smaller subset of ulcers, such as foot ulcers, with no special processing for wound images. In this paper, we build segmentation models for eight different types of wound images. Wound image analysis is a challenging problem due to the lack of availability of extensive data (labeled or unlabeled), and annotation is also challenging due to the shortage of well-trained wound care clinicians. To handle these challenges, we contribute WoundSeg 1 , a large and diverse dataset of segmented wound images. Generic wound image segmentation is complex due to the heterogeneous appearance of wound area across images of similar wound types. We propose a novel image segmentation framework, WSNet, which leverages (a) wound-domain adaptive pretraining on a large unlabeled wound image collection and (b) a global-local architecture that utilizes full image and its patches to learn fine-grained details of heterogeneous wounds. On WoundSeg, we achieve a decent Dice score of 0.847. On existing AZH Woundcare and Medetec datasets, we establish a new state-of-the-art. Further, we show the impact of using segmentation for improving the accuracy of downstream tasks like wound
