Automated deep-learning method for whole-breast segmentation in diffusion-weighted breast MRI

Lei Zhang; Ruimei Chai; Aly A. Mohamed; Bingjie Zheng; Zhimeng Luo; Shandong Wu

doi:10.1117/12.2512958

13 March 2019 Automated deep-learning method for whole-breast segmentation in diffusion-weighted breast MRI

Lei Zhang, Ruimei Chai, Aly A. Mohamed, Bingjie Zheng, Zhimeng Luo, Shandong Wu

Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 109502R (2019) https://doi.org/10.1117/12.2512958
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

Citation Download Citation

Lei Zhang, Ruimei Chai, Aly A. Mohamed, Bingjie Zheng, Zhimeng Luo, and Shandong Wu "Automated deep-learning method for whole-breast segmentation in diffusion-weighted breast MRI", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109502R (13 March 2019); https://doi.org/10.1117/12.2512958

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