  White matter (WM) tract segmentation based on diffusion magnetic resonance
imaging (dMRI) plays an important role in the analysis of human health and
brain diseases. However, the annotation of WM tracts is time-consuming and
needs experienced neuroanatomists. In this study, to explore tract segmentation
in the challenging setting of minimal annotations, we propose a novel framework
utilizing only one annotated subject (subject-level one-shot) for tract
segmentation. Our method is constructed by proposed registration-based peak
augmentation (RPA) and uncertainty-based refining (URe) modules. RPA module
synthesizes pseudo subjects and their corresponding labels to improve the tract
segmentation performance. The proposed URe module alleviates the negative
influence of the low-confidence voxels on pseudo subjects. Experimental results
show that our method outperforms other state-of-the-art methods by a large
margin, and our proposed modules are effective. Overall, our method achieves
accurate whole-brain tract segmentation with only one annotated subject. Our
code is available at
https://github.com/HaoXu0507/ISBI2023-One-Shot-WM-Tract-Segmentation.
