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。

使用缩放残差自助法在任意数据集上改进白质束分割的泛化能力

Better Generalization of White Matter Tract Segmentation to Arbitrary  Datasets with Scaled Residual Bootstrap

Deep learning based methods have achieved state-of-the-art performance for
automated white matter (WM) tract segmentation. In these methods, the
segmentation model needs to be trained with a large number of manually
annotated scans, which can be accumulated throughout time. When novel WM
tracts, i.e., tracts not included in the existing annotated WM tracts, are to
be segmented, additional annotations of these novel WM tracts need to be
collected. Since tract annotation is time-consuming and costly, it is desirable
to make only a few annotations of novel WM tracts for training the segmentation
model, and previous work has addressed this problem by transferring the
knowledge learned for segmenting existing WM tracts to the segmentation of
novel WM tracts. However, accurate segmentation of novel WM tracts can still be
challenging in the one-shot setting, where only one scan is annotated for the
novel WM tracts. In this work, we explore the problem of one-shot segmentation
of novel WM tracts. Since in the one-shot setting the annotated training data
is extremely scarce, based on the existing knowledge transfer framework, we
propose to further perform extensive data augmentation for the single annotated
scan, where synthetic annotated training data is produced. We have designed
several different strategies that mask out regions in the single annotated scan
for data augmentation. Our method was evaluated on public and in-house
datasets. The experimental results show that our method improves the accuracy
of one-shot segmentation of novel WM tracts.

本文研究如何基于深度学习对白质纤维传播成像进行分割，着重研究了如何利用少量标注数据进行一次性分割，通过数据增强的方式，提高了模型对新的白质纤维传播成像的分割准确性。