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Abstract #3555

Attention-based Semantic Segmentation of Thigh Muscle with T1-weighted Magnetic Resonance Imaging

Zihao Tang1, Kain Kyle2, Michael H Barnett2,3, Ché Fornusek4, Weidong Cai1, and Chenyu Wang2,3
1School of Computer Science, University of Sydney, Sydney, Australia, 2Sydney Neuroimaging Analysis Centre, Sydney, Australia, 3Brain and Mind Centre, University of Sydney, Sydney, Australia, 4Discipline of Exercise and Sport Science, Faculty of Medicine and Health, University of Sydney, Sydney, Australia

Robust and accurate MRI-based thigh muscle segmentation is critical for the study of longitudinal muscle volume change. However, the performance of traditional approaches is limited by morphological variance and often fails to exclude intramuscular fat. We propose a novel end-to-end semantic segmentation framework to automatically generate muscle masks that exclude intramuscular fat using longitudinal T1-weighted MRI scans. The architecture of the proposed U-shaped network follows the encoder-decoder network design with integrated residual blocks and attention gates to enhance performance. The proposed approach achieves a performance comparable with human imaging experts.

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