Automatic Pavlov ratio measurement method based on spinal landmarks identification by a deep‐learning model
This study presents a novel YOLOX-HRNet-DN model to assist landmarks identification on lateral cervical spine radiographs and demonstrates excellent accuracy in measuring the Pavlov ratio. The proposed method could provide a potential tool for the automatic estimation of the Pavlov ratio to improve the efficiency and accuracy of the treatment workflow.
Authors
- B.Q. Zhang
- Peng Cao (ORCID: https://orcid.org/0000-0001-8703-9503)
- Junhao Zhou (ORCID: https://orcid.org/0000-0002-7156-2321)
- Xueyuan Zhang (ORCID: https://orcid.org/0009-0009-3555-3020)
- Yibo Dou (ORCID: https://orcid.org/0000-0003-0802-9898)
- Xiaowen Wang (ORCID: https://orcid.org/0000-0003-1880-7921)
- Yongli Wang (ORCID: https://orcid.org/0000-0002-1369-1707)
- Fei Ren (ORCID: https://orcid.org/0000-0002-3186-3170)
- Kai Cao (ORCID: https://orcid.org/0000-0002-7939-7279)
- Chi Huang (ORCID: https://orcid.org/0009-0003-9134-3921)
- Xiyue Cheng
Institutions
- North China University of Science and Technology (CN)
- Second Military Medical University (CN)
- Institute of Computing Technology (CN)
- Shanghai Changzheng Hospital (CN)
- Changhai Hospital (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2024-12-23
- DOI
- https://doi.org/10.1002/mp.17594
- Primary Topic
- Medical Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China