  Breast cancer is one of the leading causes of cancer death among women
worldwide. In clinical routine, automatic breast ultrasound (BUS) image
segmentation is very challenging and essential for cancer diagnosis and
treatment planning. Many BUS segmentation approaches have been studied in the
last two decades, and have been proved to be effective on private datasets.
Currently, the advancement of BUS image segmentation seems to meet its
bottleneck. The improvement of the performance is increasingly challenging, and
only few new approaches were published in the last several years. It is the
time to look at the field by reviewing previous approaches comprehensively and
to investigate the future directions. In this paper, we study the basic ideas,
theories, pros and cons of the approaches, group them into categories, and
extensively review each category in depth by discussing the principles,
application issues, and advantages/disadvantages.
