Semantic Segmentation of MiceWounds
Bruno Uhlmann Marcato, Soraia Mendes Pierotti, Patricia Durgante Ritter, Camila Rodrigues Ferraz, Waldiceu Aparecido Verri Jr, Rúbia Casagrande, José Luis Seixas, Rafael Gomes Mantovani · Anais do Computer on the Beach · 2024
Semantic segmentation has been successfully explored in biologicalstudies to handle various applications, such as identifying wounds.This study explores two image segmentation approaches to identifymice wounds, specifically the U-Net and Random Forest algorithms.The latter was combined with features extracted from the first twolayers of VGG16, which was used as a feature extractor. Experimentswere performed with a real dataset developed by the Pain,Neuropathy, and Inflammation Laboratory at the State Universityof Londrina with the approval of the University Ethics Committeeon Animal Research and Welfare. The experimental results werepromising, showing that both alternatives can provide accuratepredictions for most images regarding FScore and IoU evaluationmeasures. Statistical tests were also applied, showing that U-Netobtained statistically better results with an average FScore of 0.72and IoU of 0.58.