TUS-REC Challenge 2024
Trackerless 3D Freehand Ultrasound Reconstruction (TUS-REC) Challenge
📚 TUS-REC2024 Challenge Paper Update
The TUS-REC2024 Challenge paper has been updated with a more comprehensive literature review and accompanying supplementary material. You can access the updated version here: https://doi.org/10.48550/arXiv.2506.21765 and the supplementary material here: supplementary.xlsx.
📝 TUS-REC2024 Challenge Paper Released on arXiv
We are pleased to announce that our challenge paper for the TUS-REC2024 Challenge is now released on arXiv: https://doi.org/10.48550/arXiv.2506.21765.
🔐 TUS-REC2024 Dataset Usage Policy Update
The TUS-REC2024 training and validation datasets are publicly available for research purpose as long as the challenge paper is properly cited, as specified on the Zenodo page. Please note that the TUS-REC2025 Challenge datasets are not permitted for public use yet. They are intended solely for use within the scope of the TUS-REC2025 Challenge at this moment.
🧑💻 Participant Code Repositories Available
Code repositories from TUS-REC2024 Challenge participants are now publicly accessible here.
Reconstructing 2D Ultrasound (US) images into a 3D volume enables 3D representations of anatomy to be generated which are beneficial to a wide range of downstream tasks such as quantitative biometric measurement, multimodal registration, 3D visualisation and interventional guidance. Although substantive progress has been made recently through non-deep-learning- and deep-learning-based approaches, this application is still challenging due to 1) inherent accumulated error - frame-to-frame transformation error will be accumulated through time when reconstructing long sequence of US frames, and 2) a lack of publicly-accessible data with synchronised spatial location, often obtained from tracking devices, for benchmarking the performance and for training learning-based methods. The TUS-REC challenge aims to provide a benchmark for freehand US reconstruction with publicly available in vivo US data from forearms of one hundred volunteers, using multiple predefined scanning protocols, targeted at improving the reconstruction performance in this challenging task. The outcome of the challenge includes 1) open-sourcing the first largest tracked US datasets with accurate positional information; 2) establishing one of the first benchmarks for 3D US reconstruction, suitable for modern learning-based data-driven approaches.
The TUS-REC challenge is an open call event, accepting new submissions after conference deadline. The fixed challenge submission timeline is associated with MICCAI 2024, with the following challenge timeline.
The full challenge description can be found here.
The train data is split into three parts: Part 1, Part 2, and Part 3. Validation dataset is available here.
Baseline code is provided in this repo, together with the submission/evaluation code.
Timeline
| Date | Challenge Milestone |
|---|---|
| Apr. 1 2024 | Registration Opens |
| May 13 2024 | Training Data Release |
| Jul. 29 2024 | Validation Data Release |
| Aug. 12 2024 | Submissions begins |
| Sept. 09 2024 | Submissions Closes |
| Sep. 16 2024 | Winners Announcement |
| Oct. 6 2024 | TUS-REC Challenge Event @ MICCAI 2024 |
The Task
This challenge aims to estimate the transformations among US frames, such that the entire US scan can be reconstructed in 3D space, both for establishing one of the first benchmarks for 3D US reconstruction and paving the way from experimental volunteer studies to potential clinical applications for this challenging task.
For detailed information, please refer to task description, dataset, assessment, and submission process.
Awards
The results from all participants will be made publicly available on leaderboards unless the submitted codes incurred errors during the evaluation process. Teams are allowed to make multiple distinct submissions (but must ensure they are not merely simple variations in hyperparameter values). The leaderboards will be accessible for public viewing here.
- The first-place and runner-up achievers will receive additional certificates.
- Participants who successfully participated the challenge will be awarded certificates of participation.
Organizers
Qi Li, University College London
Shaheer U. Saeed, University College London
Yuliang Huang, University College London
Dean C. Barratt, University College London
Matthew J. Clarkson, University College London
Tom Vercauteren, King’s College London
Yipeng Hu, University College London
Challenge Contact E-Mail: qi.li.21@ucl.ac.uk
Sponsors
