Automatic segmentation of esophageal cancer, metastatic lymph nodes and their adjacent structures in CTA images based on the UperNet Swin network
The UperNet Swin showed excellent efficiency in autosegmentation and volume measurement of EC, MLN and its adjacent structures in different T stage, which can help to T and N stage diagnose EC and will save clinicians time and energy.
Authors
- Yongjian Nian (ORCID: https://orcid.org/0000-0001-6779-0850)
- Wei Wu (ORCID: https://orcid.org/0000-0002-7097-2150)
- Runyuan Wang (ORCID: https://orcid.org/0000-0001-5908-6601)
- Pingnian He (ORCID: https://orcid.org/0000-0001-7346-7419)
- Jinfeng Ma (ORCID: https://orcid.org/0000-0003-3285-9357)
- Ximei Cao
- Ximing Xu (ORCID: https://orcid.org/0000-0001-7094-0514)
- Xingcai Chen (ORCID: https://orcid.org/0000-0002-9489-5014)
- Xiaoqin Zhang (ORCID: https://orcid.org/0000-0003-0254-7598)
- Yi Wu (ORCID: https://orcid.org/0000-0003-3838-7418)
- Huilin Cui
Institutions
- Army Medical University (CN)
- Shanxi Medical University (CN)
- Southwest Hospital (CN)
- Yueyang Changling Equipment Research Institute (China) (CN)
- Children's Hospital of Chongqing Medical University (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Cancer Medicine
- Published
- 2024-09-01
- DOI
- https://doi.org/10.1002/cam4.70188
- Citations
- 5
- Primary Topic
- Esophageal Cancer Research and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 2.86
Funders
- National Natural Science Foundation of China
- Chongqing Medical University