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Automated 3-D breast ultrasound (ABUS) is a newfound system for breastscreening that has been proposed as a supplementary modality to mammography forbreast cancer detection. While ABUS has better performance in dense breasts,reading ABUS images is exhausting and time-consuming. So, a computer-aideddetection system is necessary for interpretation of these images. Masssegmentation plays a vital role in the computer-aided detection systems and itaffects the overall performance. Mass segmentation is a challenging taskbecause of the large variety in size, shape, and texture of masses. Moreover,an imbalanced dataset makes segmentation harder. A novel mass segmentationapproach based on deep learning is introduced in this paper. The deep networkthat is used in this study for image segmentation is inspired by U-net, whichhas been used broadly for dense segmentation in recent years. The system'sperformance was determined using a dataset of 50 masses including 38 malign and12 benign lesions. The proposed segmentation method attained a mean Dice of0.82 which outperformed a two-stage supervised edge-based method with a meanDice of 0.74 and an adaptive region growing method with a mean Dice of 0.65.

Mass Segmentation in Automated 3-D Breast Ultrasound Using Dual-Path U-net