Automatic Area Estimation of Mice Wound Images - Marcato, Bruno Uhlmann; Ferraz, Camila Rodrigues; Verri Jr, Waldiceu Aparecido; Casagrande, Rubia; Campos, Daniel Prado; Seixas Junior, José Luis; Mantovani, Rafael Gomes (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe); 2024: Anais do XII Symposium on Knowledge Discovery, Mining and LearningDO - 10.5753/kdmile.2024.241973, 2024)
Image segmentation is a classic computer vision set of techniques that partitions a digital image into discrete groups of pixel-image segments to inform object detection and related tasks. It has been successfully explored in biological studies, such as in the identification of wounds. However, recent approaches towards using black-box deep learning algorithms for image and semantic segmentation of objects have higher computational costs than classic techniques. In this study, we evaluated the effectiveness of thresholding and deep learning techniques for semantic segmentation of wound images of mice. Experiments were performed with a real dataset developed by the Pain, Neuropathy, and Inflammation Laboratory at the State University of Londrina with the approval of the University Ethics Committee on Animal Research and Welfare. The results were promising, showing that deep learning and thresholding were able to recognize wound areas, with an average IoU of 0.75 and 0.72, respectively. However, when estimating the wound areas, deep learning results were the most close to the ground truth.