Task 1 : The automatic detection of intracranial aneurysms from Time of Flight MRAs
Task 2: The automatic segmentation of intracranial aneurysms from Time of Fight MRAs
Intracerebral aneurysms are found in 3% of the general population, and some groups have a higher risk. If an aneurysm ruptures it causes bleeding in the brain (subarachnoid haemorrhage).  Early detection of intracranial aneurysms, as well as accurate measurement and assessment of shape, is important in clinical routine. This enables careful monitoring of the growth and rupture risk of aneurysms to allow informed treatment decisions to be made .
However, intracranial aneurysm detection and measurement can sometimes be difficult – especially for small aneurysms . It has been cited that about 10% of the aneurysms, mostly small ones, are still missed . For small aneurysms (<5mm) it has been reported that detection by radiologists from MRAs can have a sensitivity as low as 35% .
The purpose of this challenge is to automatically detect and segment intracranial aneurysms from TOF-MRA images. Automatic detection can aid a radiologist in diagnosis of intracranial aneurysms and will likely speed up the clinical workflow. Volumetric segmentation allows accurate and reliable analysis of the size and shape of the aneurysms which may provide new biomarkers for use in rupture risk prediction models. Eventually, this may result in more informed decisions being made with regard to treatment of intracranial aneurysms.
A full description of the motivation behind this challenge and references can be found here.
There are two tasks in this challenge: 1) detection of unruptured intracranial aneurysms and 2) segmentation of
unruptured intracranial aneurysms from Time of Flight MRAs (TOF-MRAs). The dataset consists of 113 training cases including a TOF-MRA and a structural image for every subject. Methods will be assessed on various detection and segmentation metrics when evaluated on a secret train set. The desired algorithm output of the methods is for task 1) a location of the centre of mass of the aneurysm(s) and or task 2, a binary mask of the aneurysm(s). More details can be fund here.
Participants will containerise their algorithms with Docker and submit these to the organisers. Detailed instructions and easy-to-follow examples are provided here and, if needed, the organisers will help with containerisation. The organisers will run the submitted methods on the test data within their own institute using the evaluation code. This guarantees that the test data remains secret and cannot be included in the training procedure of the techniques.
In case of technical issues with the method (e.g. related to containerisation), we allow participants to submit fixes. After the MICCAI 2020 challenge session, the challenge will remain open for new submissions and updates of previously submitted methods.
The organisers will evaluate all methods according to the evaluation criteria. The results will be published on this website, and full details of the result announcement can be found here.
Terms of Participation
The ADAM Challenge is organised in the spirit of cooperative scientific progress. We do not claim any ownership or right to the methods, but we require anyone to respect the rules below. The following rules apply to those who register a team and/or download the data:
- The downloaded data sets, associated reference standard, or any data derived from these data sets, may not be given or redistributed under any circumstances to persons not belonging to the registered team.
- All information entered when registering a team, including the name of the contact person, the affiliation (institute, organisation or company the team’s contact person works for) and the e-mail address must be complete and correct. Anonymous or incomplete registration is not allowed. If you wish to submit anonymously, for example because you want to submit your results to a journal or conference that requires anonymous submission, please contact the organisers first.
- The data provided may only be used for preparing an entry to be submitted to this challenge. The data may not be used for other purposes in scientific studies and may not be used to train or develop other algorithms, including but not limited to algorithms used in commercial products, without prior participation in the challenge and approval by the organisers.
- Results of your submission will only be published on the website when a document describing the method is provided.
- If a commercial system is evaluated no method description is necessary, but the system has to be publicly available and the exact name and version number have to be provided.
- The organisers of the challenge will check the method description before your results will be published on the website.
- If the results of algorithms in this challenge are to be used in scientific publications (e.g. journal publications, conference papers, technical reports, presentations at conferences and meetings) you must make an appropriate citation to this challenge and the journal paper.
- Evaluation of registration results uploaded to this website will be made publicly available on this website (Results section), and by submitting results, you grant us permission to publish our evaluation. Participating teams maintain full ownership and rights to their method.
- Teams must notify the organizers of this challenge about any publication that is (partly) based on the results data published on this website, in order for us to maintain a list of publications associated with the challenge.
Click here to download this agreement as PDF. This agreement needs to be signed and mailed to email@example.com, to activate your registration and get access to the data. Please name the attached file “ADAMConfidentialtyAgreement_teamname.pdf”(where “teamname” is your own team name).
- K. Timmins, I.C. van der Schaaf, E. Bennink et al., ”Comparing methods of detecting and segmenting unruptured intracranial aneurysms on TOF-MRAS: The ADAM challenge”, NeuroImage, vol. 238, nr. 118216, 2021.
- A. Keedy, “An overview of intracranial aneurysms,” McGill Journal of Medicine, vol. 9, no. 2. pp. 141–146, 2006.
- J. M. Wardlaw and P. M. White, “The detection and management of unruptured intracranial aneurysms,” Brain, vol. 123, no. 2, pp. 205–221, 2000.
- P. M. White, J. M. Wardlaw, and V. Easton, “Can noninvasive imaging accurately depict intracranial aneurysms? A systematic review,” Radiology, vol. 217, no. 2, pp. 361–370, 2000.
- P. M. White, E. M. Teasdale, J. M. Wardlaw, and V. Easton, “Intracranial aneurysms: CT angiography and MR angiography for detection – Prospective blinded comparison in a large patient cohort,” Radiology, vol. 219, no. 3, pp. 739–749, 2001.