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