Cervical vertebral body segmentation in X-ray and magnetic resonance imaging based on YOLO-UNet: Automatic segmentation approach and available tool
The YOLO-UNet network model provided a robust and versatile solution for cervical vertebral body segmentation, demonstrating excellent accuracy and adaptability across imaging modalities on both X-ray and MRI datasets. The accompanying user-friendly tool enhanced usability, making it accessible to both clinical and research users. In this study, the challenge of large-scale medical annotation tasks was addressed, thereby reducing project costs and supporting advancements in medical information technology and clinical research.
Authors
- Yang Song (ORCID: https://orcid.org/0000-0002-6248-8086)
- Zhi Dou (ORCID: https://orcid.org/0000-0002-4553-0190)
- Jie Lu (ORCID: https://orcid.org/0000-0003-0425-3921)
- Hongyan Wang (ORCID: https://orcid.org/0000-0003-1091-9646)
- Liangliang He (ORCID: https://orcid.org/0009-0009-6516-3904)
- Xiao Yin (ORCID: https://orcid.org/0000-0002-2644-7973)
- Liqiang Yang (ORCID: https://orcid.org/0000-0002-1940-9979)
- Wenxing Zhao (ORCID: https://orcid.org/0009-0009-3656-5381)
Institutions
- Capital Medical University (CN)
- Shanghai Maritime University (CN)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251347695
- Citations
- 4
- Primary Topic
- Medical Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 2.70