Chai Chao, Wu Mengran, Wang Huiying, Cheng Yue, Zhang Shengtong, Zhang Kun, Shen Wen, Liu Zhiyang, Xia Shuang
Department of Radiology, Tianjin Institute of Imaging Medicine, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
College of Electronic Information and Optical Engineering, Nankai University, Tianjin, China.
Front Neurosci. 2022 Jun 2;16:918623. doi: 10.3389/fnins.2022.918623. eCollection 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 . 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 literature. This study 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. Experimental 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 nonuniform patch sampling contributed significantly to improving the segmentation accuracy, which indicated that appropriate choices of training and inference strategies were at least as important as designing more advanced network structures.
深部灰质核团的异常铁沉积与多种神经系统疾病相关。利用定量磁化率成像（QSM）技术，可以定量测量脑内铁含量。为了在QSM中评估深部灰质核团的磁化率，首先必须对感兴趣的核团进行分割，文献中已经提出了许多自动分割方法。本研究提出了一种用于核团分割的对比注意力U-Net，并在使用不同MRI设备、不同参数、不同序列采集的两个数据集上评估了其性能。实验结果表明，我们提出的方法在两个数据集上均优于其他常用的网络结构。此外还讨论了训练和推理策略的影响，结果表明在推理阶段采用测试时增强可以带来显著的改进。在训练阶段，我们的结果表明，充分的数据增强、深度监督和非均匀图像块采样对提高分割精度有显著贡献，这表明适当选择训练和推理策略至少与设计更先进的网络结构同样重要。
