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Abstract #2403

Deep Learning for Automated Segmentation of Brain Nuclei on Quantitative Susceptibility Mapping

Yida Wang1, Naying He2, Yan Li2, Yi Duan1, Ewart Mark Haacke2,3, Fuhua Yan2, and Guang Yang1
1East China Normal University, Shanghai Key Laboratory of Magnetic Resonance, Shanghai, China, 2Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 3Department of Biomedical Engineering, Wayne State University, Detroit, MI, United States

We proposed a deep learning (DL) method to automatically segment brain nuclei including caudate nucleus, globus pallidus, putamen, red nucleus, and substantia nigra on Quantitative Susceptibility Mapping (QSM) data. Due to the large differences of shape and size of brain nuclei, the output branches at different semantic levels in U-net++ model were designed to simultaneously output different brain nuclei. Deep supervision was applied for improving segmentation performance. The segmentation results showed the mean Dice coefficients for the five nuclei achieved a value above 0.8 in validation dataset and the trained network could accurately segment brain nuclei regions on QSM images.

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