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Proceedings Paper

Deep learning-based breast tumor detection and segmentation in 3D ultrasound image
Author(s): Yang Lei; Jincao Yao; Xiuxiu He; Dong Xu; Lijing Wang; Wei Li; Walter J. Curran; Tian Liu; Xiaofeng Yang
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Paper Abstract

Automated 3D breast ultrasound (ABUS) has substantial potential in breast imaging. ABUS appears to be beneficial because of its outstanding reproducibility and reliability, especially for screening women with dense breasts. However, due to the high number of slices in 3D ABUS, it requires lengthy screening time for radiologists, and they may miss small and subtle lesions. In this work, we propose to use a 3D Mask R-CNN method to automatically detect the location of the tumor and simultaneously segment the tumor contour. The performance of the proposed algorithm was evaluated using 25 patients’ data with ABUS image and ground truth contours. To further access the performance of the proposed method, we quantified the intersection over union (IoU), Dice similarity coefficient (DSC), and center of mass distance (CMD) between the ground truth and segmentation. The resultant IoU 96% ± 2%, DSC 84% ± 3%, and CMD 1.95 ± 0.89 mm respectively, which demonstrated the high accuracy of tumor detection and 3D volume segmentation of the proposed Mask R-CNN method. We have developed a novel deep learning-based method and demonstrated its capability of being used as a useful tool for computer-aided diagnosis and treatment.

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11319, Medical Imaging 2020: Ultrasonic Imaging and Tomography, 113190Y (16 March 2020); doi: 10.1117/12.2549157
Show Author Affiliations
Yang Lei, Winship Cancer Institute of Emory Univ. (United States)
Jincao Yao, Institute of Cancer and Basic Medicine (China)
Cancer Hospital of the Univ. of Chinese Academy of Sciences (China)
Zhejiang Cancer Hospital (China)
Xiuxiu He, Winship Cancer Institute of Emory Univ. (United States)
Dong Xu, Institute of Cancer and Basic Medicine (China)
Cancer Hospital of the Univ. of Chinese Academy of Sciences (China)
Zhejiang Cancer Hospital (China)
Lijing Wang, Institute of Cancer and Basic Medicine (China)
Cancer Hospital of the Univ. of Chinese Academy of Sciences (China)
Zhejiang Cancer Hospital (China)
Wei Li, Institute of Cancer and Basic Medicine (China)
Cancer Hospital of the Univ. of Chinese Academy of Sciences (China)
Zhejiang Cancer Hospital (China)
Walter J. Curran, Winship Cancer Institute of Emory Univ. (United States)
Tian Liu, Winship Cancer Institute of Emory Univ. (United States)
Xiaofeng Yang, Winship Cancer Institute of Emory Univ. (United States)


Published in SPIE Proceedings Vol. 11319:
Medical Imaging 2020: Ultrasonic Imaging and Tomography
Brett C. Byram; Nicole V. Ruiter, Editor(s)

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