The UTrack framework for segmenting and measuring dermatological ulcers through telemedicine

• Published in: Computers in biology and medicine Vol. 134; p. 104489
• Main Authors: Cazzolato, Mirela T., Ramos, Jonathan S., Rodrigues, Lucas S., Scabora, Lucas C., Chino, Daniel Y.T., Jorge, Ana E.S., de Azevedo-Marques, Paulo Mazzoncini, Traina, Caetano, Traina, Agma J.M.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01-07-2021; Elsevier Limited
• Subjects: Algorithms; Applications programs; Cameras; Deep learning; Dermatological ulcers; Electronic devices; Evolution; Healing; Image processing; Image resolution; Image segmentation; Medical imaging; Medical personnel; mHealth; Mobile; Mobile computing; Patients; Segmentation; Severe acute respiratory syndrome coronavirus 2; Skin; Telemedicine; Ulcers; Units of measurement; Wound healing; Wound measurement; Wounds; Segmentation; Image processing; Telemedicine; mHealth; Dermatological ulcers; Wound measurement; Mobile
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2021.104489

Chronic dermatological ulcers cause great discomfort to patients, and while monitoring the size of wounds over time provides significant clues about the healing evolution and the clinical condition of patients, the lack of practical applications in existing studies impairs users’ access to appropriate treatment and diagnosis methods. We propose the UTrack framework to help with the acquisition of photos, the segmentation and measurement of wounds, the storage of photos and symptoms, and the visualization of the evolution of ulcer healing. UTrack-App is a mobile app for the framework, which processes images taken by standard mobile device cameras without specialized equipment and stores all data locally. The user manually delineates the regions of the wound and the measurement object, and the tool uses the proposed UTrack-Seg segmentation method to segment them. UTrack-App also allows users to manually input a unit of measurement (centimeter or inch) in the image to improve the wound area estimation. Experiments show that UTrack-Seg outperforms its state-of-the-art competitors in ulcer segmentation tasks, improving F-Measure by up to 82.5% when compared to superpixel-based approaches and up to 19% when compared to Deep Learning ones. The method is unsupervised, and it semi-automatically segments real-world images with 0.9 of F-Measure, on average. The automatic measurement outperformed the manual process in three out of five different rulers. UTrack-App takes at most 30 s to perform all evaluation steps over high-resolution images, thus being well-suited to analyze ulcers using standard mobile devices.
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•Telemedicine and mHealth are relevant tools to aid chronic dermatological ulcers.•Chronic dermatological ulcers affect patients with different health conditions.•Periodic wound size measurement gives meaningful clues about the ulcer healing evolution.•UTrack-App aids ulcer patients' follow-up regarding wound size and symptoms' frequency.•UTrack-App has a visualization module to track the ulcer size and symptoms' evolution.