Automated 3D breast ultrasound (ABUS) has substantial potential in breast imaging. ABUS appears to be beneficialbecause of its outstanding reproducibility and reliability, especially for screening women with dense breasts. However,due to the high number of slices in 3D ABUS, it requires lengthy screening time for radiologists, and they may miss smalland subtle lesions. In this work, we propose to use a 3D Mask R-CNN method to automatically detect the location of thetumor and simultaneously segment the tumor contour. The performance of the proposed algorithm was evaluated using 25patients’ data with ABUS image and ground truth contours. To further access the performance of the proposed method,we quantified the intersection over union (IoU), Dice similarity coefficient (DSC), and center of mass distance (CMD)between the ground truth and segmentation. The resultant IoU 96% ± 2%, DSC 84% ± 3%, and CMD 1.95 ± 0.89 mmrespectively, which demonstrated the high accuracy of tumor detection and 3D volume segmentation of the proposed MaskR-CNN method. We have developed a novel deep learning-based method and demonstrated its capability of being used asa useful tool for computer-aided diagnosis and treatment.
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