Abstract
Automated breast ultrasound (ABUS) is a new and promising imaging modality for breast cancer detection and diagnosis, which could provide intuitive 3D information and coronal plane information with great diagnostic value. However, manually screening and diagnosing tumors from ABUS images is very time-consuming and overlooks of abnormalities may happen. In this study, we propose a novel two-stage 3D detection network for locating suspected lesion areas and further classifying lesions as benign or malignant tumors. Specifically, we propose a 3D detection network rather than frequently-used segmentation network to locate lesions in ABUS images, thus our network can make full use of the spatial context information in ABUS images. A novel similarity loss is designed to effectively distinguish lesions from background. Then a classification network is employed to identify the located lesions as benign or malignant. An IoU-balanced classification loss is adopted to improve the correlation between classification and localization task. The efficacy of our network is verified from a collected dataset of 418 patients with 145 benign tumors and 273 malignant tumors. Experiments show our network attains a sensitivity of 97.66% with 1.23 false positives (FPs), and has an area under the curve(AUC) value of 0.8720.
J. Yu and C. Chen—Contribute equally to this work.
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Acknowledgements
This work was supported in part by the National Key R&D Program of China (No. 2019YFC0118300), in part by the National Natural Science Foundation of China under Grant 61701312, in part by the Guangdong Basic and Applied Basic Research Foundation (2019A1515010847), in part by the Medical Science and Technology Foundation of Guangdong Province (B2019046), in part by the Natural Science Foundation of SZU (No. 860-000002110129), and in part by the Shenzhen Peacock Plan (KQTD2016053112051497).
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Yu, J. et al. (2020). Computer-Aided Tumor Diagnosis in Automated Breast Ultrasound Using 3D Detection Network. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12266. Springer, Cham. https://doi.org/10.1007/978-3-030-59725-2_18
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