A Benchmark for Breast Ultrasound Image Segmentation (BUSIS)

Background

Breast cancer is one of the leading causes of cancer death among women, and one in eight women in the United States will develop breast cancer during their lifetime. In clinical routine, the tumor segmentation is a critical but quite challenging step for further cancer diagnosis and treatment planning.

Many BUS segmentation approaches have been proposed in the last two decades, but the performances of most approaches have been assessed using relatively small private datasets with different quantitative metrics, which result in the discrepancy in performance comparison. Therefore, there is a pressing need for building a benchmark to compare existing methods using a public dataset objectively, and 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, we will publish a B-mode BUS image segmentation benchmark (BUSIS) with 562 images and compare the performance of fourteen state-of-the-art BUS segmentation methods quantitatively.

Dataset, Ground Truth and Metrics

Basic Dataset Information

The 562 images are collected and prepared by the joint efforts of scientists and researchers from the following institutions:

  • Utah State University
  • University of Idaho
  • Harbin Institute of Technology
  • The Second Affiliated Hospital of Harbin Medical University
  • The Affiliated Hospital of Qingdao University
  • The Second Hospital of Hebei Medical University

The images were from different sources and collected using multiple ultrasound devices:

  • GE VIVID 7
  • LOGIQ E9
  • Hitachi EUB-6500
  • Philips iU22
  • Siemens ACUSON S2000

Informed consents to the protocol from all patients were acquired. The privacy of the patients is well protected.

Ground Truth Generation

Four experienced radiologists are involved in ground truth generation: three radiologists read and delineate each tumor boundary individually, and the fourth (senior expert) will determine whether the majority-voting results need adjustment. The complete procedures of the ground truth generation are as follows.

  1. Each of the three experienced radiologists manually delineates each tumor boundary, and three delineation results will be produced for each BUS image.
  2. View all pixels inside/on the boundary as the tumor region, and outside pixels as the background; conduct majority voting to generate the preliminary result for each BUS image.
  3. A senior expert will review each BUS image and refer to its corresponding preliminary result to determine whether any adjustment is needed.
  4. Label tumor pixel as 1 and background pixel as 0; and generate a binary and uncompressed image to save the ground truth for each BUS image.

Release Agreement Download

If you would like to access this dataset, please sign the dataset release agreement and email us. We will send you the dataset link upon receipt of the signed agreement. Please allow up to 10 business days for processing.

BUSIS Data Set Agreement

References

If you use the dataset and results of this work, please cite the following papers:

  1. M. Xian, Y. Zhang, and H. D. Cheng, “Fully automatic segmentation of breast ultrasound images based on breast characteristics in space and frequency domains,” Pattern Recognit., vol. 48, no. 2, pp. 485-497, 2015.
  2. H. D. Cheng, J. Shan, W. Ju, Y. Guo, and L. Zhang, “Automated breast cancer detection and classification using ultrasound images: A survey,” Pattern Recognit., vol. 43, no. 1, pp. 299-317, Jan, 2010.
  3. Y. Zhang, M. Xian, H. D. Cheng, B. Shareef, J. Ding, F. Xu, K. Huang, B. Zhang, C. Ning, Y. Wang, "BUSIS: A Benchmark for Breast Ultrasound Image Segmentation," Healthcare., vol. 10, no. 4, pp. 729, Apr, 2022.
  4. M. Xian, Y. Zhang, H. D. Cheng, F. Xu, B. Zhang, and J. Ding, "Automatic Breast Ultrasound Image Segmentation: A Survey," Pattern Recognit., vol. 79, pp. 340-355, 2018.

Main Contributors

  • D. Cheng, Fei Xu, and Kuan Huang | Utah State University
  • Min Xian, Boyu Zhang | University of Idaho
  • Yingtao Zhang, Jianrui Ding | Harbin Institute of Technolgy
  • Chunping Ning | The Affiliated Hospital of Qingdao University
  • Ying Wang | The Second Hospital of Hebei Medical University