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.

本研究提出了一种通过使用缩放残差自助法改进的白质束分割方法，以提高模型对任意测试数据集的泛化性能。实验证明，在不同设置下，我们的方法一致提高了白质束分割的泛化能力。