  Breast ultrasound (BUS) image segmentation is challenging and critical for
BUS Comput-er-Aided Diagnosis (CAD) systems. Many BUS segmentation approaches
have been studied in the last two decades, but the performances of most
approaches have been assessed using relatively small private datasets with
different quantitative metrics, which results in a discrepancy in performance
comparison. Therefore, there is a pressing need for building a benchmark to
compare existing methods using a public dataset objectively, to determine the
performance of the best breast tumor segmentation algorithm available today,
and to investigate what segmentation strategies are valuable in clinical
practice and theoretical study. In this work, a benchmark for B-mode breast
ultrasound image segmentation is presented. In the benchmark, 1) we collected
562 breast ultrasound images, prepared a software tool, and involved four
radiologists in obtaining accurate annotations through standardized procedures;
2) we extensively compared the performance of sixteen state-of-the-art
segmentation methods and discussed their advantages and disadvantages; 3) we
proposed a set of valuable quantitative metrics to evaluate both semi-automatic
and fully automatic segmentation approaches; and 4) the successful segmentation
strategies and possible future improvements are discussed in details.
