Abstract #5271

The Multi-Class Segmentation of the Human Cerebral Vasculature in TOF-MRA: A Supervised Deep Learning Approach

Karim Fathy¹, Felix Dumais², Samantha Côté ¹, Blaise Frederick³, and Kevin Whittingstall⁴

¹Departement of Biomedical Imaging and Radiation Science, Universite de Sherbrooke, Sherbrooke, QC, Canada, ²Departement of Computer Science, Universite de Sherbrooke, Sherbrooke, QC, Canada, ³Departement of Psychiatry, Harvard Medical School, Belmont, MA, United States, ⁴Departement of Radiology, Universite de Sherbrooke, Sherbrooke, QC, Canada

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Neuro, Software ToolCurrently, there is no accurate fully automated multi-class method to segment the whole cerebral arterial tree in time-of-flight magnetic resonance angiography (TOF-MRA). We developed an artificial intelligence based software tool to identify cerebral arteries in TOF-MRAs. We trained a neural network on a TOF-MRA dataset and labeled the cerebral arterial tree using different image processing techniques. Our software tool is fast and reliable, with no human intervention, and allows for the conduction of large-scale TOF-MRA studies while being versatile in segmenting a diverse set of TOF-MRAs.

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