Simultaneous vessel segmentation and unenhanced prediction using self-supervised dual-task learning in 3D CTA (SVSUP)
- Resource Type
- ACADEMIC JOURNAL
- Authors
- Huang, Wenjian a, 1; Gao, Weizheng a; Hou, Chao b; Zhang, Xiaodong b; Wang, Xiaoying a, b, ⁎; Zhang, Jue a, c, ⁎
- Source
- In Computer Methods and Programs in Biomedicine September 2022 224
- Subject
- Language
- English
- ISSN
- 0169-2607
- E-ISSN
- DOI
- 10.1016/j.cmpb.2022.107001
Highlights •Proposed an iterative residual-sharing scheme based dual-task learning framework for vessel segmentation and unenhanced CT prediction.•Presented a pseudo-labeling based self-supervised strategy for vessel segmentation using contrasting modalities (pre/post-contrast imaging), which avoids labor-intensive manual labeling of training sets.•Verified the feasibility of unenhanced CT prediction, which has the potential to eliminate the pre-contrast CT scan for radiation-dose reduction.•The vessel segmentation results obtained by the proposed dual-task method are superior compared to popular 3D vessel segmentation models and deep-learning segmentation methods.