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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