U-Net-Based Models for Precise Brain Stroke Segmentation

Abstract

Ischemic stroke, a widespread neurological condition with a substantial mortality rate, necessitates accurate delineation of affected regions to enable proper evaluation of patient outcomes. However, such precision is complicated by factors like variable lesion sizes, noise interference, and the overlapping intensity characteristics of different tissue structures. This research addresses these issues by focusing on the segmentation of Diffusion Weighted Imaging (DWI) scans from the ISLES 2022 dataset and conducting a comparative assessment of three advanced deep learning models: the U-Net framework, its U-Net++ extension, and the Attention U-Net. Applying consistent evaluation criteria specifically, Intersection over Union (IoU), Dice Similarity Coefficient (DSC), and recall the Attention U-Net emerged as the superior choice, establishing record high values for IoU (0.8223) and DSC (0.9021). Although U-Net achieved commendable recall, its performance lagged behind that of U-Net++ in other critical measures. These findings underscore the value of integrating attention mechanisms to achieve more precise segmentation. Moreover, they highlight that the Attention U-Net model is a reliable candidate for medical imaging tasks where both accuracy and efficiency hold paramount importance, while U Net and U Net++ may still prove suitable in certain niche scenarios.