Automatic Breast Ultrasound Image Segmentation: A Survey
It provides a comprehensive review for researchers and clinicians in medical imaging, but is incremental as it summarizes existing work without introducing new methods.
This paper surveys automatic breast ultrasound image segmentation methods used over the last two decades for breast cancer diagnosis, noting that performance improvements have become increasingly challenging with few new approaches recently.
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.