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.

翻译：基于深度学习的方法在自动化白质(W M)纤维束分割中取得了最先进的性能。在这类方法中，分割模型需要用大量手动标注的扫描图像进行训练，这些图像可以随时间累积。当需要分割新型白质纤维束（即现有标注白质纤维束中未包含的纤维束）时，需要收集这些新型纤维束的额外标注。由于纤维束标注既耗时又昂贵，因此希望仅使用少量新型纤维束标注来训练分割模型。此前的研究通过将现有纤维束分割任务中学到的知识迁移至新型纤维束分割来解决该问题。然而，在单次分割场景下（即仅有一例扫描图像对新型纤维束进行了标注），准确分割新型纤维束仍具挑战性。本研究探索了新型白质纤维束的单次分割问题。针对单次设置中标注训练数据极度匮乏的情况，我们在现有知识迁移框架基础上，进一步对单一标注扫描图像进行大量数据增强，生成合成标注训练数据。我们设计了多种遮蔽单例标注扫描图像中不同区域的策略以实现数据增强。本方法在公开数据集和内部数据集上进行了评估，实验结果表明，该方法提高了新型白质纤维束单次分割的准确性。