CAU-Net: A Deep Learning Method for Deep Gray Matter Nuclei Segmentation - Chai, Chao; Wu, Mengran; Wang, Huiying; Cheng, Yue; Zhang, Shengtong; Zhang, Kun; Shen, Wen; Liu, Zhiyang; Xia, Shuang (Frontiers in Neuroscience, 2022)
The abnormal iron deposition of the deep gray matter nuclei is related to many neurological diseases. With the quantitative susceptibility mapping (QSM) technique, it is possible to quantitatively measure the brain iron content in vivo. To assess the magnetic susceptibility of the deep gray matter nuclei in the QSM, it is mandatory to segment the nuclei of interest first, and many automatic methods have been proposed in the literatures. This paper proposed a contrast attention U-Net for nuclei segmentation and evaluated its performance on two datasets acquired using different sequences with different parameters from different MRI devices. Experiment results revealed that our proposed method was superior on both datasets over other commonly adopted network structures. The impacts of training and inference strategies were also discussed, which showed that adopting test time augmentation during the inference stage can impose an obvious improvement. At the training stage, our results indicated that sufficient data augmentation, deep supervision, and non-uniform patch sampling contributed significantly to improving the segmentation accuracy, which indicated that proper choices of training and inference strategies were at least as important as designing more advanced network structures.