Automated Segmentation of Coronary Arteries


Data Access:

Thank you for your interest in this challenge. While we’re planning on keeping the challenge website and evaluation server operating, we are waiting for the challenge and data description manuscripts to be published before allowing new participants. This avoids a lengthy publication embargo period on methods developed using this challenge data. If you have already emailed us, we will contact you as soon as this available. This page will also be updated.

If you wish to use the data solely for participation in this challenge, Please sign up on the grand-challenges website, then fill out the data confidentiality form and email a scanned copy of the signed form to r.gharleghi@student.unsw.edu.au .

For other research using this dataset, access will be provided using the Synapse platform. We are still working on making this data available for other research purposes, we expect the data to be available at https://www.synapse.org/ASOCA in the oncoming months.

Data

You are provided with a training set of 40 Cardiac Computed Tomography Angiography (CCTA) with contrast agent showing the coronary arteries, comprising of 20 healthy patients and 20 patients with confirmed coronary artery disease. Annotations produced by three expert annotators are provided for this training set.

Further 20 CCTA images (10 healthy and 10 patients with disease) will be released as the test set used for evaluation of the segmentation algorithms. Ground truth for the test set will not be provided while the challenge is in progress.

Task

The task of this challenge is to segment the coronary artery lumen; not including calcified regions or other disease present. The developed methods must be fully automatic. Dice score and 95% Hausdorff distance will be used to evaluate the produced results.

Rules:

Refer to the detailed challenge description.


Results:

The top 10 participants will presesnt their results at MICCAI 2020, 8th October 2020: (09:00-13:00 UTC )

Contact:

Organisers:

  • Ramtin Gharleghi
  • Gihan Samarasinghe
  • Arcot Sowmya
  • Susann Beier