We hope that you find one or more of the CUBDL-related resources useful for your own work. Our four resources include:

• https://cubdl.jhu.edu/
  (i.e., this website)
• CUBDL Datasets
• CUBDL Code
• CUBDL Data Guide

If you use any of the above resources, citations to the following three references are required:

1. MAL Bell, J Huang, D Hyun, YC Eldar, R van Sloun, M Mischi, “Challenge on Ultrasound Beamforming with Deep Learning (CUBDL)”, Proceedings of the 2020 IEEE International Ultrasonics Symposium, 2020 [pdf]
2. Muyinatu A. Lediju Bell, Jiaqi Huang, Alycen Wiacek, Ping Gong, Shigao Chen, Alessandro Ramalli, Piero Tortoli, Ben Luijten, Massimo Mischi, Ole Marius Hoel Rindal, Vincent Perrot , Hervé Liebgott, Xi Zhang, Jianwen Luo, Eniola Oluyemi, Emily Ambinder, “Challenge on Ultrasound Beamforming with Deep Learning (CUBDL) Datasets”, IEEE DataPort, 2019 [Online]. Available: http://dx.doi.org/10.21227/f0hn-8f92
3. D. Hyun, A. Wiacek, S. Goudarzi, S. Rothlübbers, A. Asif, K. Eickel, Y. C. Eldar, J. Huang, M. Mischi, H. Rivaz, D. Sinden, R.J.G. van Sloun, H. Strohm, M. A. L. Bell, Deep Learning for Ultrasound Image Formation: CUBDL Evaluation Framework & Open Datasets, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 68(12):3466-3483, 2021[pdf]

Datasets are now available for release by visiting the CUBDL DataPort site.

Peruse the latest research referencing our resources here [3], here [1] and here [2].