Liu Wan, Lu Qi, Zhuo Zhizheng, Li Yuxing, Duan Yunyun, Yu Pinnan, Qu Liying, Ye Chuyang, Liu Yaou
School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China.
Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Neuroimage. 2022 Apr 15;250:118934. doi: 10.1016/j.neuroimage.2022.118934. Epub 2022 Jan 26.
Convolutional neural networks have achieved state-of-the-art performance for white matter (WM) tract segmentation based on diffusion magnetic resonance imaging (dMRI). However, the segmentation can still be difficult for challenging WM tracts with thin bodies or complicated shapes; the segmentation is even more problematic in challenging scenarios with reduced data quality or domain shift between training and test data, which can be easily encountered in clinical settings. In this work, we seek to improve the segmentation of WM tracts, especially for challenging WM tracts in challenging scenarios. In particular, our method is based on volumetric WM tract segmentation, where voxels are directly labeled without performing tractography. To improve the segmentation, we exploit the characteristics of WM tracts that different tracts can cross or overlap and revise the network design accordingly. Specifically, because multiple tracts can co-exist in a voxel, we hypothesize that the different tract labels can be correlated. The tract labels at a single voxel are concatenated as a label vector, the length of which is the number of tract labels. Due to the tract correlation, this label vector can be projected into a lower-dimensional space-referred to as the embedded space-for each voxel, which allows the segmentation network to solve a simpler problem. By predicting the coordinate in the embedded space for the tracts at each voxel and subsequently mapping the coordinate to the label vector with a reconstruction module, the segmentation result can be achieved. To facilitate the learning of the embedded space, an auxiliary label reconstruction loss is integrated with the segmentation accuracy loss during network training, and network training and inference are end-to-end. Our method was validated on two dMRI datasets under various settings. The results show that the proposed method improves the accuracy of WM tract segmentation, and the improvement is more prominent for challenging tracts in challenging scenarios.
卷积神经网络在基于扩散磁共振成像（dMRI）的白质（WM）纤维束分割方面取得了最先进的性能。然而，对于具有细纤维束或复杂形状的具有挑战性的WM纤维束，分割仍然可能很困难；在数据质量降低或训练与测试数据之间存在域偏移的具有挑战性的场景中，分割问题更加严重，而这些情况在临床环境中很容易遇到。在这项工作中，我们试图改进WM纤维束的分割，特别是针对具有挑战性的场景中的具有挑战性的WM纤维束。具体而言，我们的方法基于体素WM纤维束分割，其中体素直接被标记，而无需进行纤维束成像。为了改进分割，我们利用了WM纤维束的特征，即不同的纤维束可以交叉或重叠，并相应地修改网络设计。具体来说，由于多个纤维束可以共存于一个体素中，我们假设不同的纤维束标签可以相互关联。单个体素处的纤维束标签被连接成一个标签向量，其长度是纤维束标签的数量。由于纤维束的相关性，这个标签向量可以被投影到每个体素的一个低维空间——称为嵌入空间，这使得分割网络能够解决一个更简单的问题。通过预测每个体素处纤维束在嵌入空间中的坐标，随后使用重建模块将坐标映射到标签向量，就可以得到分割结果。为了促进嵌入空间的学习，在网络训练期间，将辅助标签重建损失与分割精度损失相结合，并且网络训练和推理是端到端的。我们的方法在两个dMRI数据集的各种设置下进行了验证。结果表明，所提出的方法提高了WM纤维束分割的准确性，并且对于具有挑战性的场景中的具有挑战性的纤维束，这种提高更为显著。
