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Two fully automated data-driven 3D whole-breast segmentation strategies in MRI for MR-based breast density using image registration and U-Net with a focus on reproducibility

Crossref DOI link: https://doi.org/10.1186/s42492-022-00121-4

Published Online: 2022-10-11

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Ying, Jia

Cattell, Renee

Zhao, Tianyun

Lei, Lan

Jiang, Zhao

Hussain, Shahid M.

Gao, Yi

Chow, H.-H. Sherry

Stopeck, Alison T.

Thompson, Patricia A.

Huang, Chuan https://orcid.org/0000-0001-6052-0663
Funding

Funding for this research was provided by:

National Institutes of Health (R03CA223052, R01CA161534, R01CA172444)

University of Arizona Cancer Center - Cancer Center Support Grant (P30CA023074)
License Information

Text and Data Mining valid from 2022-10-11

Version of Record valid from 2022-10-11
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Article History

Received: 23 June 2022

Accepted: 21 September 2022

First Online: 11 October 2022

Declarations

:

: The authors declare no competing interests.

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