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White matter (WM) tract segmentation based on diffusion magnetic resonanceimaging (dMRI) plays an important role in the analysis of human health andbrain diseases. However, the annotation of WM tracts is time-consuming andneeds experienced neuroanatomists. In this study, to explore tract segmentationin the challenging setting of minimal annotations, we propose a novel frameworkutilizing only one annotated subject (subject-level one-shot) for tractsegmentation. Our method is constructed by proposed registration-based peakaugmentation (RPA) and uncertainty-based refining (URe) modules. RPA modulesynthesizes pseudo subjects and their corresponding labels to improve the tractsegmentation performance. The proposed URe module alleviates the negativeinfluence of the low-confidence voxels on pseudo subjects. Experimental resultsshow that our method outperforms other state-of-the-art methods by a largemargin, and our proposed modules are effective. Overall, our method achievesaccurate whole-brain tract segmentation with only one annotated subject. Ourcode is available athttps://github.com/HaoXu0507/ISBI2023-One-Shot-WM-Tract-Segmentation.

A Registration- and Uncertainty-based Framework for White Matter Tract Segmentation With Only One Annotated Subject