  The cerebrovascular tree is a complex anatomical structure that plays a
crucial role in the brain irrigation. A precise identification of the
bifurcations in the vascular network is essential for understanding various
cerebral pathologies. Traditional methods often require manual intervention and
are sensitive to variations in data quality. In recent years, deep learning
techniques, and particularly autoencoders, have shown promising performances
for feature extraction and pattern recognition in a variety of domains. In this
paper, we propose two novel approaches for vascular bifurcation identification
based respectiveley on Autoencoder and geometrical features. The performance
and effectiveness of each method in terms of classification of vascular
bifurcations using medical imaging data is presented. The evaluation was
performed on a sample database composed of 91 TOF-MRA, using various evaluation
measures, including accuracy, F1 score and confusion matrix.
