Enhancing Glioma Segmentation Accuracy Using Attention ResUNet

Motivation: The increasing prevalence of glioma brain tumors underscores the necessity for advanced segmentation techniques to improve diagnosis and treatment. Goal(s): This study aims to introduce Attention ResUNet (AResUNet), a novel model designed to enhance the segmentation of glioma brain tumors. Approach: AResUNet integrates attention mechanisms within a residual UNet framework and employs the BraTS 2021 dataset for training and evaluation. Results: The model achieves superior mean Dice scores compared to various state-of-the-art models, demonstrating its potential to enhance clinical processes related to brain tumor identification and treatment planning. Impact: AResUNet demonstrates improved segmentation performance for glioma brain tumors, offering insights that may enhance diagnostic accuracy and treatment strategies in clinical practice. This model's architecture showcases the benefits of integrating attention mechanisms in deep learning approaches for medical image analysis.