BUSIS: A Benchmark for Breast Ultrasound Image Segmentation

• Published in: Healthcare (Basel) Vol. 10; no. 4; p. 729
• Main Authors: Zhang, Yingtao, Xian, Min, Cheng, Heng-Da, Shareef, Bryar, Ding, Jianrui, Xu, Fei, Huang, Kuan, Zhang, Boyu, Ning, Chunping, Wang, Ying
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 14-04-2022; MDPI
• Subjects: Algorithms; benchmark; Breast cancer; breast ultrasound (BUS) images; computer-aided diagnosis (CAD); Datasets; Deep learning; Machine learning; Medical diagnosis; Medical screening; Review; segmentation; Support vector machines; Tumors; Ultrasonic imaging; breast ultrasound (BUS) images; segmentation; computer-aided diagnosis (CAD); benchmark
• ISSN: 2227-9032; 2227-9032
• DOI: 10.3390/healthcare10040729

Breast ultrasound (BUS) image segmentation is challenging and critical for BUS computer-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 and proposed standardized procedures to obtain accurate annotations using four radiologists; (2) we extensively compared the performance of 16 state-of-the-art segmentation methods and demonstrated that most deep learning-based approaches achieved high dice similarity coefficient values (DSC ≥ 0.90) and outperformed conventional approaches; (3) we proposed the losses-based approach to evaluate the sensitivity of semi-automatic segmentation to user interactions; and (4) the successful segmentation strategies and possible future improvements were discussed in details.