Comparative analysis of U-Mamba and no new U-Net for the detection and segmentation of esophageal cancer in contrast-enhanced computed tomography images
The DL methods demonstrated exceptional sensitivity and robustness in EC detection and segmentation on contrast-enhanced CT images, not only reducing missed EC diagnoses but also providing radiologists with consistent lesion annotations.
Authors
- Xin Han (ORCID: https://orcid.org/0000-0001-5282-9927)
- Lin Kong (ORCID: https://orcid.org/0000-0002-5263-5740)
- Zeyu Tang (ORCID: https://orcid.org/0000-0003-3789-2906)
- Shanshan Jiang (ORCID: https://orcid.org/0000-0003-2853-9991)
- Yifan Hu (ORCID: https://orcid.org/0009-0006-4951-985X)
- Zhihan Xu (ORCID: https://orcid.org/0000-0003-4057-8299)
- Yi Zhang (ORCID: https://orcid.org/0000-0002-5867-9322)
- Lei Zhang (ORCID: https://orcid.org/0000-0002-7528-420X)
- Xiaojin Yu
- Huimin Hong
Institutions
- Shanghai Jiao Tong University (CN)
- Nantong University (CN)
- Philips (China) (CN)
- Affiliated Hospital of Nantong University (CN)
- Shanghai First People's Hospital (CN)
- Siemens Healthcare (United States) (US)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2025-03-01
- DOI
- https://doi.org/10.21037/qims-24-1116
- Primary Topic
- Esophageal Cancer Research and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00