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Abstract #2662

Automatic multilabel segmentation of large cerebral vessels from MR angiography images using deep learning

Félix Dumais1, Marco Perez Caceres1, Noémie Arès-Bruneau2, Christian Bocti2,3,4, and Kevin Whittingstall5
1Médecine nucléaire et radiobiologie, Université de Sherbrooke, Sherbrooke, QC, Canada, 2Faculté de Médecine et des Sciences de la Santé, Université de Sherbrooke, Sherbrooke, QC, Canada, 3Clinique de la Mémoire et Centre de Recherche sur le Vieillissement, CIUSSS de l’Estrie-CHUS, Sherbrooke, QC, Canada, 4Service de Neurologie, Département de Médecine, CHUS, Sherbrooke, QC, Canada, 5Radiologie diagnostique, Université de Sherbrooke, Sherbrooke, QC, Canada

The Circle of Willis (CW) is a system of arteries located at the base of the brain. Its structure is a key component in different cerebrovascular diseases, though quantifying this on MR images is meticulous and labor intensive. We developed an analysis pipeline that uses a convolutional neural network to do multilabel segmentation of the CW as viewed through TOF-MRA images. Results show that this approach can automatically locate and label the CW with the same accuracy as expert human annotators.

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