Challenge on Ultrasound Beamforming with Deep Learning (CUBDL) Datasets

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Submission Dates:
10/14/2019 to 09/08/2021
Citation Author(s):
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:
Mon, 07/19/2021 - 08:40
DOI:
10.21227/f0hn-8f92
Links:
License:
Creative Commons Attribution

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 (accepted July 1, 2021) [pdf]

 

Comments

Are there any test datasets available to get started with?

Yes, please sign up to request access.

As i don't have signed agreement so what can I do to access the data sets

Thanks for your inquiry. The agreement is available for you to sign and submit with your access request.

Im working on US & Deep Learning

Thanks for your interest. There seems to be a problem with your form. Can you please try resubmitting your request with your true identity?

I've submitted the signed agreement. How can I get access ? Thanks.

Your access was already granted. Please check your email for details and possibly check your spam folder too.

Do I need to send an e-mail address that's linked to onedrive? I can't find where I can access the dataset on the IEEEDataPort web page.

You have the correct understanding. If you are still experiencing issues, please send an email to receive more troubleshooting assistance.

thanks

Excellent, you’re welcome!

duplicate post -- deleted

Dataset Files

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Documentation

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File CUBDL Data Release Agreement.pdf985.81 KB