100 Data for Training
|25 Data for Testing|
3D Gadolinium-Enhanced Magnetic Resonance Imaging
3D Binary Masks of the Left Atrial Cavity
Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. The poor performance of current AF treatment is due to a lack of understanding of the structure of the human atria.
Nowadays, gadolinium contrast agencies are used in a third of all MRI scans to improve the clarity of the images of a patient's internal structures, such as the atria. Gadolinium-enhanced magnetic resonance imaging (GE-MRI) is widely used to study the extent of fibrosis (scars) across the atria . Recent studies on human atria imaged with GE-MRI have suggested fingerprints of the atrial structure may hold the key to understanding and reversing AF .
Direct segmentation of the atrial chambers from GE-MRIs is very challenging due to the low contrast between the atrial tissue and background. Most of the existing atrial structural analysis studies utilizing GE-MRIs have been based on labor-intensive, error/bias-prone manual segmentation. Hence, there is a need for an intelligent algorithm that can perform fully automatic atrial segmentation for the left atrial (LA) cavity, to accurately reconstruct and visualize the atrial structure for clinical usage.
This challenge has provided an open competition for wider communities to test and validate their methods for image segmentation on a large 3D clinical dataset. The exciting development is a very important step towards patient-specific diagnostics and treatment of AF.
The participants are required to produce a computational framework capable of performing fully automatic segmentation of the LA cavity from 3D GE-MRIs without any manual assistance. The participants will receive 100 data+masks to develop their approach and be evaluated on 25 different test data for evaluation. The participants will submit the masks of the LA cavity for the 25 patients. The evaluation will be done by comparing the submitted masks for the test data the with their manually segmented masks (not open to the public). The test data (data only) will be released 2 weeks prior to the deadline of the challenge so participants can submit their predicted masks. This challenge provides a chance for participants to carefully study and experiment on a large GE-MRI dataset, and further push the state-of-the-art performance for atria segmentation.
To download the dataset and other relevant files, please visit the Data section of the challenge.
To submit your predictions for the challenge, please visit the Submission section of the challenge.
- 4 April 2018: Competition begins (training data released)
- 28 June 2018: Workshop paper submission deadline
- 23 July 2018: Workshop paper notification
- 1 Sep 2018: Competition open for submission (test data released)
- 14 Sep 2018: Competition ends
- 16 Sep 2018: Workshop (final winner announced)
All deadlines are at 11:59 PM UTC on the corresponding day unless otherwise noted. The competition organizers reserve the right to update the contest timeline if they deem it necessary.
- The top 3 participant teams will receive prize money from a total prize pool of $800 USD.
- The top participants will be invited to participate in a subsequent challenge at Kaggle ($500 USD total prize pool) which is to be announced.
- Finally, the top performers will be invited to co-author in an international benchmarking study.
*The MICCAI conference will be held from September 16th to 20th 2018 in Granada, Spain, during which the STACOM workshop will be held on 16 September, 2018. To qualify for the prize, the potential participants will need to submit a workshop paper and attend the STACOM workshop (Springer submission system will be open in May).
The challenge is proudly sponsored by:
- The MedTech Centre of Research Excellence (New Zealand)
- Auckland Bioengineering Institute (University of Auckland)
- Arterys (Medical Imaging Cloud AI)
 Christopher McGann, Nazem Akoum, Amit Patel, Eugene Kholmovski, Patricia Revelo, Kavitha Damal, Brent Wilson, Josh Cates, Alexis Harrison, Ravi Ranjan, Nathan S. Burgon, Tom Greene, Dan Kim, Edward V.R. DiBella, Dennis Parker, Rob S. MacLeod, Nassir F. Marrouche. "Atrial fibrillation ablation outcome is predicted by left atrial remodeling on MRI." Circulation: Arrhythmia and Electrophysiology (2013): CIRCEP-113.
 Brian J. Hansen, Jichao Zhao, Thomas A. Csepe, Brandon T. Moore, Ning Li, Laura A. Jayne, Anuradha Kalyanasundaram, Praise Lim, Anna Bratasz, Kimerly A. Powell, Orlando P. Simonetti, Robert S.D. Higgins, Ahmet Kilic, Peter J. Mohler, Paul M.L. Janssen, Raul Weiss, John D. Hummel, Vadim V. Fedorov. "Atrial fibrillation driven by micro-anatomic intramural re-entry revealed by simultaneous sub-epicardial and sub-endocardial optical mapping in explanted human hearts." European heart journal 36, no. 35 (2015): 2390-2401.
 Jichao Zhao, Brian J. Hansen, Yufeng Wang, Thomas A. Csepe, Lidiya V. Sul, Alan Tang, Yiming Yuan, Ning Li, Anna Bratasz, Kimerly A. Powell, Ahmet Kilic, Peter J. Mohler, Paul M. L. Janssen, Raul Weiss, Orlando P. Simonetti, John D. Hummel, Vadim V. Fedorov. "Three‐dimensional Integrated Functional, Structural, and Computational Mapping to Define the Structural “Fingerprints” of Heart‐Specific Atrial Fibrillation Drivers in Human Heart Ex Vivo." Journal of the American Heart Association 6, no. 8 (2017): p.e005922.
For any personal questions, please contact the organizers at this email address: firstname.lastname@example.org.