Challenge on Ultrasound Beamforming with Deep Learning (CUBDL) Datasets
- Submission Dates:
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to
- Citation Author(s):
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Muyinatu Bell (Johns Hopkins University, USA)Jiaqi Huang (Johns Hopkins University, USA)Alycen Wiacek (Johns Hopkins University, USA)Ping Gong (Mayo Clinic, USA)Shigao Chen (Mayo Clinic, USA)Alessandro Ramalli (University of Florence, Italy)Piero Tortoli (University of Florence, Italy)Ben Luijten (Eindhoven University of Technology, The Netherlands)Massimo Mischi (Eindhoven University of Technology, The Netherlands)Ole Marius Hoel Rindal (University of Oslo, Norway)Vincent Perrot (Creatis, University of Lyon, INSA, France)Hervé Liebgott (Creatis, University of Lyon, INSA, France)Xi Zhang (Tsinghua University, China)Jianwen Luo (Tsinghua University, China)Eniola Oluyemi (Johns Hopkins Medicine, USA)Emily Ambinder (Johns Hopkins Medicine, USA)
- Submitted by:
- Muyinatu Lediju Bell
- Last updated:
- DOI:
- 10.21227/f0hn-8f92
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Abstract
The purpose of this challenge is to provide standardization of methods for assessing and benchmarking deep learning approaches to ultrasound image formation from ultrasound channel data that will live beyond the challenge.
Instructions:
- Participants had the freedom to create their own training data to build networks that accomplish specified tasks; this option is still available now that the challenge is closed.
- Specified tasks and evaluation methods are described on the challenge website: https://cubdl.jhu.edu/
- Participant submissions were facilitated by IEEE DataPort while the challenge was open
- Data sharing is facilitated by IEEE DataPort
Although the challenge is now closed, evaluation code remains available (more details on the challenge website https://cubdl.jhu.edu/), and datasets are available for release by submitting a signed user agreement (be sure to include all pages).
Dataset Details:
The following journal paper describes dataset details, top challenge submissions, and the evaluation process implemented by the challenge organizers:
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, vol. 68, no. 12, pp. 3466-3483, Dec. 2021, doi: 10.1109/TUFFC.2021.3094849 [pdf]
Access Dataset:
There seems to be an issue with requesting data. If you agree to abide by all terms in the CUBDL Data Release Agreement.pdf, then click here to confirm your agreement and simultaneously access the associated data.
Are there any test datasets available to get started with?
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I'm studying on beamforming and I need access
There seems to be an issue with requesting data. If you agree to abide by all terms in the CUBDL Data Release Agreement.pdf, then click here to confirm your agreement and simultaneously access the associated data.
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