White matter (WM) tract segmentation is a crucial step for brain connectivity studies. It is performed on diffusion magnetic resonance imaging (dMRI), and deep neural networks (DNNs) have achieved promising segmentation accuracy. Existing DNN-based methods use an annotated dataset for model training. However, the performance of the trained model on a different test dataset may not be optimal due to distribution shift, and it is desirable to design WM tract segmentation approaches that allow better generalization of the segmentation model to arbitrary test datasets. In this work, we propose a WM tract segmentation approach that improves the generalization with scaled residual bootstrap. The difference between dMRI scans in training and test datasets is most noticeably caused by the different numbers of diffusion gradients and noise levels. Since both of them lead to different signal-to-noise ratios (SNRs) between the training and test data, we propose to augment the training scans by adjusting the noise magnitude and develop an adapted residual bootstrap strategy for the augmentation. To validate the proposed approach, two dMRI datasets were used, and the experimental results show that our method consistently improved the generalization of WM tract segmentation under various settings.

翻译：白质纤维束分割是脑连接研究中的关键步骤，该任务基于扩散磁共振成像（dMRI）进行，且深度神经网络（DNNs）已实现了令人瞩目的分割精度。现有基于DNN的方法使用标注数据集进行模型训练。然而，由于分布偏移，训练后的模型在不同测试数据集上的性能可能并非最优，因此有必要设计能提升分割模型对任意测试数据集泛化能力的白质纤维束分割方法。本研究提出一种通过缩放残差自举法改善泛化性能的白质纤维束分割方法。训练集与测试集dMRI扫描之间的差异最显著地源于扩散梯度数量和噪声水平的不同。由于两者都会导致训练数据与测试数据信噪比（SNR）的差异，我们提出通过调整噪声幅度来增强训练扫描，并为此增强过程开发了一种自适应残差自举策略。为验证所提方法，我们使用了两个dMRI数据集，实验结果表明，我们的方法在各种设置下均能持续提升白质纤维束分割的泛化性能。