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Two-stage CNNs for computerized BI-RADS categorization in breast ultrasound images
Yunzhi Huang1,2; Luyi Han2; Haoran Dou3; Honghao Luo4; Zhen Yuan5; Qi Liu2; Jiang Zhang2; Guangfu Yin1

Background: Quantizing the Breast Imaging Reporting and Data System (BI-RADS) criteria into different categories with the single ultrasound modality has always been a challenge. To achieve this, we proposed a two-stage grading system to automatically evaluate breast tumors from ultrasound images into five categories based on convolutional neural networks (CNNs).

Methods: This new developed automatic grading system was consisted of two stages, including the tumor identification and the tumor grading. The constructed network for tumor identification, denoted as ROI-CNN, can identify the region contained the tumor from the original breast ultrasound images. The following tumor categorization network, denoted as G-CNN, can generate effective features for differentiating the identified regions of interest (ROIs) into five categories: Category "3", Category "4A", Category "4B", Category "4C", and Category "5". Particularly, to promote the predictions identified by the ROI-CNN better tailor to the tumor, refinement procedure based on Level-set was leveraged as a joint between the stage and grading stage.

Results: We tested the proposed two-stage grading system against 2238 cases with breast tumors in ultrasound images. With the accuracy as an indicator, our automatic computerized evaluation for grading breast tumors exhibited a performance comparable to that of subjective categories determined by physicians. Experimental results show that our two-stage framework can achieve the accuracy of 0.998 on Category "3", 0.940 on Category "4A", 0.734 on Category "4B", 0.922 on Category "4C", and 0.876 on Category "5".

Conclusion: The proposed scheme can extract effective features from the breast ultrasound images for the final classification of breast tumors by decoupling the identification features and classification features with different CNNs. Besides, the proposed scheme can extend the diagnosing of breast tumors in ultrasound images to five subcategories according to BI-RADS rather than merely distinguishing the breast tumor malignant from benign.

KeywordBreast Tumor In Ultrasound Image Breast Imaging Reporting And Data System (Bi-rads) Automatic Categorization Deep Convolutional Neural Network
Indexed BySCIE
WOS Research AreaEngineering
WOS SubjectEngineering, Biomedical
WOS IDWOS:000459861600001
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Cited Times [WOS]:9   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Health Sciences
Corresponding AuthorQi Liu; Guangfu Yin
Affiliation1.Department of Biomedical Engineering, College of Materials Science and Engineering, Sichuan University, Chengdu, 610065, China
2.College of Electrical Engineering and Information Technology, Sichuan University, Chengdu, 610065, China
3.National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, Shenzhen, 518060, China
4.Department of Ultrasound, West China Hospital of Sichuan University, Chengdu, 610041, China
5.Bioimaging Core, Faculty of Health Sciences, University of Macau, Macau SAR, China
Recommended Citation
GB/T 7714
Yunzhi Huang,Luyi Han,Haoran Dou,et al. Two-stage CNNs for computerized BI-RADS categorization in breast ultrasound images[J]. BIOMEDICAL ENGINEERING ONLINE,2019,18(8).
APA Yunzhi Huang,Luyi Han,Haoran Dou,Honghao Luo,Zhen Yuan,Qi Liu,Jiang Zhang,&Guangfu Yin.(2019).Two-stage CNNs for computerized BI-RADS categorization in breast ultrasound images.BIOMEDICAL ENGINEERING ONLINE,18(8).
MLA Yunzhi Huang,et al."Two-stage CNNs for computerized BI-RADS categorization in breast ultrasound images".BIOMEDICAL ENGINEERING ONLINE 18.8(2019).
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