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. 2019 May;98(19):e15200.
doi: 10.1097/MD.0000000000015200.

Detection and classification the breast tumors using mask R-CNN on sonograms

Jui-Ying Chiao  1 , Kuan-Yung Chen  2 , Ken Ying-Kai Liao  3 , Po-Hsin Hsieh  1 , Geoffrey Zhang  4 , Tzung-Chi Huang  1   3   5
Affiliations

Detection and classification the breast tumors using mask R-CNN on sonograms

Jui-Ying Chiao et al. Medicine (Baltimore). 2019 May.

Abstract

Breast cancer is one of the most harmful diseases for women with the highest morbidity. An efficient way to decrease its mortality is to diagnose cancer earlier by screening. Clinically, the best approach of screening for Asian women is ultrasound images combined with biopsies. However, biopsy is invasive and it gets incomprehensive information of the lesion. The aim of this study is to build a model for automatic detection, segmentation, and classification of breast lesions with ultrasound images. Based on deep learning, a technique using Mask regions with convolutional neural network was developed for lesion detection and differentiation between benign and malignant. The mean average precision was 0.75 for the detection and segmentation. The overall accuracy of benign/malignant classification was 85%. The proposed method provides a comprehensive and noninvasive way to detect and classify breast lesions.

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Conflict of interest statement

The authors have no conflicts of interest to disclose.

Figures

Figure 1
Figure 1
Breast ultrasound images of (a) benign lesion, (b) malignant tumor.
Figure 2
Figure 2
The network architecture of Mask R-CNN. RoIAlign replaces RoI Pooling in Mask R-CNN, and the mask branch is consecutively used to mark the result of RoIAlign. Gray flow chart is the original Faster R-CNN, and the red one is differences and amendments between Mask R-CNN and Faster R-CNN. R-CNN = regions with convolutional neural network, RoI = region of interest, RoIAlign = region of interest alignment, RoIPool = region of interest pooling.
Figure 3
Figure 3
Example of tumor contour. (a, b) An original image of malignant tumor and contour mask (white area); (c, d) an original image of benign tumor and contour mask (white area).
Figure 4
Figure 4
Example of lesion segmentation evaluation. (a) A benign lesion; (b) the radiologist delineated the red contour (solid line), and the rectangular box was calculated according to the manual contour (dashed line); (c) the automatic lesion delineation by the proposed method. The confident score for this case was 0.992.
None
See this image and copyright information in PMC

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