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 Bocti^2,3,4, 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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