Benchmark dataset for clot detection in ischemic stroke vessel-based imaging: CODEC-IV
Freda Werdiger,
Milanka Visser,
Andrew Bivard,
Xingjuan Li,
Sunay Gotla,
Angelos Sharobeam,
Michael Valente,
James Beharry,
Vignan Yogendrakumar,
Mark W. Parsons
Affiliations
Freda Werdiger
Melbourne Brain Centre at Royal Melbourne Hospital, Melbourne, Australia; Department of Medicine, University of Melbourne, Melbourne, Australia; Corresponding author.
Milanka Visser
Melbourne Brain Centre at Royal Melbourne Hospital, Melbourne, Australia; Department of Medicine, University of Melbourne, Melbourne, Australia
Andrew Bivard
Melbourne Brain Centre at Royal Melbourne Hospital, Melbourne, Australia; Department of Medicine, University of Melbourne, Melbourne, Australia
Xingjuan Li
Southwestern Sydney Clinical School, University of New South Wales, Sydney, Australia; Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia
Sunay Gotla
Apollo Medical Imaging, Melbourne, Australia
Angelos Sharobeam
Melbourne Brain Centre at Royal Melbourne Hospital, Melbourne, Australia; Department of Medicine, University of Melbourne, Melbourne, Australia
Michael Valente
Melbourne Brain Centre at Royal Melbourne Hospital, Melbourne, Australia; Department of Medicine, University of Melbourne, Melbourne, Australia
James Beharry
Melbourne Brain Centre at Royal Melbourne Hospital, Melbourne, Australia
Vignan Yogendrakumar
Melbourne Brain Centre at Royal Melbourne Hospital, Melbourne, Australia; Department of Medicine, University of Melbourne, Melbourne, Australia
Mark W. Parsons
Southwestern Sydney Clinical School, University of New South Wales, Sydney, Australia; Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia; Department of Neurology, Liverpool Hospital, NSW, Australia
We present an annotated dataset for the purposes of creating a benchmark in Artificial Intelligence for automated clot detection. While there are commercial tools available for automated clot detection on computed tomographic (CT) angiographs, they have not been compared in a standardized manner whereby accuracy is reported on a publicly available benchmark dataset. Furthermore, there are known difficulties in automated clot detection – namely, cases where there is robust collateral flow, or residual flow and occlusions of the smaller vessels – and it is necessary to drive an initiative to overcome these challenges. Our dataset contains 159 multiphase CTA patient datasets, derived from CTP and annotated by expert stroke neurologists. In addition to images where the clot is marked, the expert neurologists have provided information about clot location, hemisphere and the degree of collateral flow. The data is available on request by researchers via an online form, and we will host a leaderboard where the results of clot detection algorithms on the dataset will be displayed. Participants are invited to submit an algorithm to us for evaluation using the evaluation tool, which is made available at together with the form at https://github.com/MBC-Neuroimaging/ClotDetectEval.
