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Deep-learning method for tumor segmentation in breast DCE-MRI
Author(s): Lei Zhang; Zhimeng Luo; Ruimei Chai; Dooman Arefan; Jules Sumkin; Shandong Wu
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Paper Abstract

Breast magnetic resonance imaging (MRI) plays an important role in high-risk breast cancer screening, clinical problemsolving, and imaging-based outcome prediction. Breast tumor segmentation in MRI is an essential step for quantitative radiomics analysis, where automated and accurate tumor segmentation is needed but very challenging. Manual tumor annotation by radiologists requires medical knowledge and is time-consuming, subjective, prone to error, and inter-user inconsistency. Several recent studies have shown the ability of deep-learning models in image segmentation. In this work, we investigated a deep-learning based method to segment breast tumors in Dynamic Contrast-Enhanced MRI (DCE-MRI) scans in both 2D and 3D settings. We implemented our method and evaluated its performance on a dataset of 1,246 breast MR images by comparing the segmentation to the manual annotations from expert radiologists. Experimental results showed that the deep-learning-based methods exhibit promising performance with the best Dice Coefficient of 0.92 ± 0.02.

Paper Details

Date Published: 15 March 2019
PDF: 6 pages
Proc. SPIE 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, 109540F (15 March 2019); doi: 10.1117/12.2513090
Show Author Affiliations
Lei Zhang, Univ. of Pittsburgh (United States)
Zhimeng Luo, Univ. of Pittsburgh (United States)
Ruimei Chai, Liaoning Cancer Hospital & Institute (China)
Dooman Arefan, Univ. of Pittsburgh (United States)
Jules Sumkin, Univ. of Pittsburgh (United States)
Shandong Wu, Univ. of Pittsburgh (United States)


Published in SPIE Proceedings Vol. 10954:
Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications
Po-Hao Chen; Peter R. Bak, Editor(s)

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