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

Autor:	Xu, Hao, Xue, Tengfei, Liu, Dongnan, Zhang, Fan, Westin, Carl-Fredrik, Kikinis, Ron, O'Donnell, Lauren J., Cai, Weidong
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted by The IEEE International Symposium on Biomedical Imaging (ISBI) 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.14371 Zobrazit plný text záznamu View this record from Arxiv