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Two-stage CNNs for computerized BI-RADS categorization in breast ultrasound images.

Abstract：

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 sub-categories according to BI-RADS rather than merely distinguishing the breast tumor malignant from benign.

journal_name

Biomed Eng Online

journal_title

Biomedical engineering online

authors

Huang Y,Han L,Dou H,Luo H,Yuan Z,Liu Q,Zhang J,Yin G

doi

10.1186/s12938-019-0626-5

subject

Has Abstract

pub_date

2019-01-24 00:00:00

pages

issue

issn

1475-925X

pii

10.1186/s12938-019-0626-5

journal_volume

pub_type

杂志文章

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Two-stage CNNs for computerized BI-RADS categorization in breast ultrasound images.