  Abnormal iron accumulation in the brain subcortical nuclei has been reported
to be correlated to various neurodegenerative diseases, which can be measured
through the magnetic susceptibility from the quantitative susceptibility
mapping (QSM). To quantitively measure the magnetic susceptibility, the nuclei
should be accurately segmented, which is a tedious task for clinicians. In this
paper, we proposed a double-branch residual-structured U-Net (DB-ResUNet) based
on 3D convolutional neural network (CNN) to automatically segment such brain
gray matter nuclei. To better tradeoff between segmentation accuracy and the
memory efficiency, the proposed DB-ResUNet fed image patches with high
resolution and the patches with low resolution but larger field of view into
the local and global branches, respectively. Experimental results revealed that
by jointly using QSM and T$_\text{1}$ weighted imaging (T$_\text{1}$WI) as
inputs, the proposed method was able to achieve better segmentation accuracy
over its single-branch counterpart, as well as the conventional atlas-based
method and the classical 3D-UNet structure. The susceptibility values and the
volumes were also measured, which indicated that the measurements from the
proposed DB-ResUNet are able to present high correlation with values from the
manually annotated regions of interest.
