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Automated deep-learning method for whole-breast segmentation in diffusion-weighted breast MRI
Author(s): Lei Zhang; Ruimei Chai; Aly A. Mohamed; Bingjie Zheng; Zhimeng Luo; Shandong Wu
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Paper Abstract

The essential sequences in breast magnetic resonance imaging (MRI) are the dynamic contrast-enhanced (DCE) images, which are widely used in clinical settings. Diffusion-weighted imaging (DWI) MRI also plays an important role in many diagnostic applications and in developing novel imaging bio-makers. Compared to DCE MRI, technical advantages of DWI include a shorter acquisition time, no need for administration of any contrast agent, and availability on most commercial scanners. Segmenting the whole-breast region is an essential pre-processing step in many quantitative and radiomics breast MRI studies. However, it is a challenging task for computerized methods due to the low contrast of intensity along breast chest wall boundaries. While several studies have reported computational methods for automated whole-breast segmentation in DCE MRI, the segmentation in DWI MRI is still underdeveloped. In this paper, we propose to use deep learning and transfer learning methods to segment the whole-breast in DWI MRI, by leveraging pretraining on a DCE MRI dataset. Experiments are reported in multiple breast MRI datasets including an external evaluation dataset and encouraging results are demonstrated.

Paper Details

Date Published: 13 March 2019
PDF: 6 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109502R (13 March 2019); doi: 10.1117/12.2512958
Show Author Affiliations
Lei Zhang, Univ. of Pittsburgh (United States)
Ruimei Chai, Liaoning Cancer Hospital & Institute (China)
Aly A. Mohamed, Univ. of Pittsburgh (United States)
Bingjie Zheng, Henan Cancer Hospital, Affiliated Cancer Hospital of Zhengzhou Univ. (China)
Zhimeng Luo, Univ. of Pittsburgh (United States)
Shandong Wu, Univ. of Pittsburgh (United States)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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