Using deep learning for an automatic detection and classification of the vascular bifurcations along the Circle of Willis

TL;DR

This paper presents a deep learning-based method for automatic detection and classification of vascular bifurcations in the Circle of Willis to aid in early diagnosis of intracranial aneurysms.

Contribution

It introduces a fully automatic neural network approach for identifying high-risk bifurcations in MRI scans, improving diagnostic efficiency.

Findings

High bifurcation recognition accuracy achieved

Multiple neural network architectures evaluated

Potential to assist neuroradiologists in diagnosis

Abstract

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.