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.
翻译:对BUS Comput-er-Aid Dialogis(CAD)系统来说,超乳性超声波图解析(CAD)系统具有挑战性和关键意义。在过去二十年中,已经研究了许多BUS分离法,但大多数方法的性能评估使用了相对较小的私人数据集,具有不同的量化指标,从而导致性能比较不一致。因此,迫切需要建立一个基准,以客观地比较使用公共数据集的现有方法,确定目前现有最佳乳腺癌分解算法的性能,并调查临床实践和理论研究中哪些是有价值的分解战略。在这项工作中,提出了B-mode乳房超声波图解析的基准。在基准中,1)我们收集了562个超声波乳房图像,制作了一个软件工具,有4名放射学家参与通过标准化程序获得准确的描述;2)我们广泛比较了16个最新分解法的性能,并讨论了其优劣之处;3)我们提出了一套宝贵的定量指标,用以评价半自动和完全自动分解析方法;以及4)成功的分解战略和可能的未来细节。