Optimization of Brain Tumor Segmentation on Magnetic Resonance Imaging (MRI) Using Attention Gate U-Net

Abstract

Abstract. Brain tumor segmentation using Magnetic Resonance Imaging (MRI) plays a vital role in medical diagnosis, requiring high precision to support clinical decisions and reduce mortality rates. 

Purpose: This research aims to enhance the segmentation process by implementing an Attention Gate into the U-Net model.

Methods/Study design/approach: In the segmentation stage, Attention Gate on U-Net is integrated to filter out relevant information from the extracted features, resulting in a more precise segmentation of the brain tumor to determine the location of the tumor. 

Result/Findings: The performance of the model is assessed by calculating several evaluation metrics such as dice coefficient and intersection-over-union (IoU) for the segmentation process. The results showed that adding Attention Gate to the U-Net achieved a dice coefficient of 87.08% and IoU of 72.70%

Novelty/Originality/Value: The novelty of this study lies in the integration of the Attention Gate mechanism within the U-Net decoder stage to enhance focus on tumor regions. While U-Net is widely used in medical image segmentation, this specific attention-based enhancement significantly improves performance compared to conventional U-Net models without attention. This research contributes to advancing more accurate and efficient decision-support systems in the field of medical image analysis.