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Abstract #1707

Enhancing Glioma Segmentation Accuracy Using Attention ResUNet

Farzan Moodi1, Fereshteh Khodadadi Shoushtari2, Gelareh Valizadeh 3, Dornaz Mazinani3, Hanieh Mobarak Salari 3, and Hamidreza Saligheh Rad4
1Quantitative MR Imaging and Spectroscopy Group (QMISG), Tehran University of Medical Sciences, Tehran, Iran-School of Medicine, Iran University of Medical Sciences, Tehran, Iran, Tehran, Iran (Islamic Republic of), 2Quantitative MR Imaging and Spectroscopy Group (QMISG), Tehran University of Medical Sciences, Tehran, Iran-Nuclear Engineering Department, Shiraz University, Shiraz, Iran, Ahwaz, Iran (Islamic Republic of), 3Quantitative MR Imaging and Spectroscopy Group (QMISG), Tehran University of Medical Sciences, Tehran, Tehran, Iran (Islamic Republic of), 4Quantitative MR Imaging and Spectroscopy Group (QMISG), Tehran, Iran (Islamic Republic of)

Synopsis

Keywords: Diagnosis/Prediction, AI/ML Software, BraTS, Brain Tumor, Deep Learning, AResUNet, Segmentation, Glioma

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.

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