Cer-ConvN3Unet: an end-to-end multi-parametric MRI-based pipeline for automated detection and segmentation of cervical cancer
An AI-assisted multiparametric MRI-based pipeline can effectively support radiologists in cervical cancer evaluation. The proposed pipeline shows high recognition and segmentation performance without manual intervention. The proposed pipeline may become a promising auxiliary tool in gynecological imaging.
Authors
- Weiwei Li (ORCID: https://orcid.org/0000-0001-5481-5908)
- Qingmo Yang (ORCID: https://orcid.org/0000-0002-6904-9772)
- Jiaqi Wu (ORCID: https://orcid.org/0000-0001-7737-5597)
- Xiangxing Kong
- Zhi‐Nan Wang
- Ying‐Shi Sun (ORCID: https://orcid.org/0000-0001-9424-1910)
- Shudong Xia (ORCID: https://orcid.org/0000-0001-6228-5783)
- Xiaoyan Zhang
- Xiao-Ting Li (ORCID: https://orcid.org/0000-0003-2758-3843)
- Kun Cao (ORCID: https://orcid.org/0000-0002-0685-300X)
- Yingming Li
- Haitao Zhu
- Bo Zhao (ORCID: https://orcid.org/0000-0002-2120-2571)
Institutions
- Peking University (CN)
- National Medical Products Administration (CN)
- Peking University Cancer Hospital (CN)
- Peking University People's Hospital (CN)
- Ministry of Education (ET)
Publication Details
- Journal
- European Radiology Experimental
- Published
- 2025-02-18
- DOI
- https://doi.org/10.1186/s41747-025-00557-2
- Citations
- 4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 5.44
Funders
- Peking University
- National Key Research and Development Program of China