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

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

Abstract Presence of higher breast density (BD) and persistence over time are risk factors for breast cancer. A quantitatively accurate and highly reproducible BD measure that relies on precise and reproducible whole-breast segmentation is desirable. In this study, we aimed to develop a highly repro...

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Bibliographic Details
Main Authors: Jia Ying, Renee Cattell, Tianyun Zhao, Lan Lei, Zhao Jiang, Shahid M. Hussain, Yi Gao, H.-H. Sherry Chow, Alison T. Stopeck, Patricia A. Thompson, Chuan Huang
Format: Article
Language:English
Published: SpringerOpen 2022-10-01
Series:Visual Computing for Industry, Biomedicine, and Art
Subjects:
Breast cancer
Breast density
Breast segmentation
Image registration
Deep learning
Online Access:https://doi.org/10.1186/s42492-022-00121-4

Internet

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

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