Automated deep learning method for whole-breast segmentation in contrast-free quantitative MRI
The nnU-Net exhibited exceptional segmentation performance for fully automated breast segmentation of contrast-free quantitative images. This method serves as an effective tool for processing large-scale clinical datasets and represents a significant advancement toward computer-aided quantitative analysis of breast DWI and SyMRI images.
Authors
- Yuelang Zhang (ORCID: https://orcid.org/0000-0002-8967-6474)
- Xuan Su
- Weibo Gao (ORCID: https://orcid.org/0000-0003-2122-8234)
- Feng Shi (ORCID: https://orcid.org/0000-0003-1522-9943)
- Quanxin Yang (ORCID: https://orcid.org/0000-0002-4064-886X)
- Wenbin Liang (ORCID: https://orcid.org/0000-0002-9086-4974)
- Xiaohui Li
- Yanyan Zhang (ORCID: https://orcid.org/0009-0009-2730-0816)
- Tuo He
- Bo Gao (ORCID: https://orcid.org/0000-0002-7177-7701)
- Yuwei Xia
- Guangxu Han
Institutions
- Second Affiliated Hospital of Xi'an Jiaotong University (CN)
- United Imaging Healthcare (China) (CN)
Publication Details
- Journal
- BMC Medical Imaging
- Published
- 2025-09-26
- DOI
- https://doi.org/10.1186/s12880-025-01928-2
- Primary Topic
- MRI in cancer diagnosis
- Type
- article
- Field-Weighted Citation Impact
- 0.00