The Challenge on Ultrasound Beamforming with Deep Learning (CUBDL) is offered as a component of the 2020 IEEE International Ultrasonics Symposium.
Recent developments in deep learning have created immense potential for ultrasound imaging research. CUBDL is designed to explore the benefits of using deep learning for both focused and plane wave transmissions. We challenge the participants to obtain the best image quality under the fastest possible frame rates.
To be considered as a challenge participant, the following three documents are needed (to be submitted via IEEE DataPort):
- One .onnx model file containing network structure and trained weights
- A diagram of the network architecture and where it fits into the entire beamforming pipeline
- A paper summarizing details of the deep learning network, including the rationale for design choices that are ideally linked to ultrasound imaging physics. Paper submissions should follow IEEE IUS 2020 proceedings guidelines.
All accepted participants are required to prepare a poster for presentation during the CUBDL workshop at IEEE IUS on September 8, 2020. Posters will remain visible during this 3-hour challenge summary workshop, as well as during the entire symposium.
The top 4-8 finishers will receive a cash prize (sponsored by Verasonics) and will have the opportunity to deliver oral presentations about their network architectures during this workshop.
- Muyinatu Bell, Johns Hopkins University
- Jiaqi (Justina) Huang, Johns Hopkins University
- Dongwoon Hyun, Stanford University
- Yonina Eldar, Weizmann Institute of Science
- Ruud van Sloun, Eindhoven University of Technology
- Massimo Mischi, Eindhoven University of Technology
Visit the CUBDL website for more details.