BUSIS: A Benchmark for Breast Ultrasound Image Segmentation

TL;DR

This paper introduces a comprehensive benchmark for breast ultrasound image segmentation, providing a public dataset, standardized evaluation metrics, and a comparison of sixteen methods to advance clinical and research applications.

Contribution

The work creates a public dataset, standardizes evaluation procedures, and systematically compares multiple segmentation algorithms for breast ultrasound images.

Findings

Identified the most effective segmentation strategies.

Provided a benchmark dataset with expert annotations.

Analyzed advantages and disadvantages of existing methods.

Abstract

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.