Using deep learning for an automatic detection and classification of the
  vascular bifurcations along the Circle of Willis
Rafic Nader Romain Bourcier Florent Autrusseau
Most of the intracranial aneurysms (ICA) occur on a specific portion of the
cerebral vascular tree named the Circle of Willis (CoW). More particularly,
they mainly arise onto fifteen of the major arterial bifurcations constituting
this circular structure. Hence, for an efficient and timely diagnosis it is
critical to develop some methods being able to accurately recognize each
Bifurcation of Interest (BoI). Indeed, an automatic extraction of the
bifurcations presenting the higher risk of developing an ICA would offer the
neuroradiologists a quick glance at the most alarming areas. Due to the recent
efforts on Artificial Intelligence, Deep Learning turned out to be the best
performing technology for many pattern recognition tasks. Moreover, various
methods have been particularly designed for medical image analysis purposes.
This study intends to assist the neuroradiologists to promptly locate any
bifurcation presenting a high risk of ICA occurrence. It can be seen as a
Computer Aided Diagnosis scheme, where the Artificial Intelligence facilitates
the access to the regions of interest within the MRI. In this work, we propose
a method for a fully automatic detection and recognition of the bifurcations of
interest forming the Circle of Willis. Several neural networks architectures
have been tested, and we thoroughly evaluate the bifurcation recognition rate.