Automatic breast ultrasound tumor segmentation via one-stage hierarchical target activation network

Tonghe Wang; Yang Lei; Marian Axente; Jincao Yao; Jolinta Lin; Jeffrey D. Bradley; Tian Liu; Dong Xu; Xiaofeng Yang

doi:10.1117/12.2611129

4 April 2022 Automatic breast ultrasound tumor segmentation via one-stage hierarchical target activation network

Tonghe Wang, Yang Lei, Marian Axente, Jincao Yao, Jolinta Lin, Jeffrey D. Bradley, Tian Liu, Dong Xu, Xiaofeng Yang

Proceedings Volume 12038, Medical Imaging 2022: Ultrasonic Imaging and Tomography; 120380L (2022) https://doi.org/10.1117/12.2611129
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Automatic breast ultrasound (ABUS) imaging is a well-established tool in breast cancer diagnosis. Delineating lesion on ABUS is an essential step for breast cancer computer-aided diagnosis (CAD). This work aims to develop an automated deep learning-based method for breast tumor segmentation on three-dimensional ABUS. The proposed method, one-stage hierarchical target activation network, consists of three subnetworks, i.e., fully convolutional one-state object detector (FCOS), hierarchical block and mask module. Feature extractor is used to extract informative features from ABUS. FCOS is used to locate the volume-of-interest (VOIs) of breast tumor. Hierarchical block is used to enhance the feature contrast around tumor boundary. Mask module then segment tumor from the refined feature map within the VOIs. A five-fold cross-validation on 40 patients’ cases was conducted. The ABUS breast tumors were segmented and compared with manual contours using several segmentation measurements. The Dice similarity coefficient (DSC) and mean surface distance (HD95) is 0.855±0.090 and 1.56±1.02 mm, respectively. These results demonstrate the feasibility and efficacy of the proposed method for breast tumor segmentation, which can further facilitate CAD for breast cancer using 3D ABUS imaging.

Conference Presentation

Citation Download Citation

Tonghe Wang, Yang Lei, Marian Axente, Jincao Yao, Jolinta Lin, Jeffrey D. Bradley, Tian Liu, Dong Xu, and Xiaofeng Yang "Automatic breast ultrasound tumor segmentation via one-stage hierarchical target activation network", Proc. SPIE 12038, Medical Imaging 2022: Ultrasonic Imaging and Tomography, 120380L (4 April 2022); https://doi.org/10.1117/12.2611129

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