Regional Multiscale Motion Representation for Cardiac Disease Prediction

Abstract

Heart characterization is a challenging task due to the non-linear dynamic performance and the strong shape deformation during the cardiac cycle. This work presents a regional multiscale motion representation of cardiac structures that is able to recognize pathologies on cine-MRI sequences. Firstly, a dense optical flow that considers large displacements was computed to obtain a velocity field representation. Then, regional dynamic patterns are coded into a multiscale scheme, from coarse to fine, emerging the most relevant cardiac patterns that remain along the different scales. The resulting motion descriptor is then formed by a set of flow orientation occurrences computed in whole multiscale regions. This descriptor is mapped to a previously trained Random forest classifier to obtain a prediction of the cardiac condition. The proposed strategy was evaluated over a set of 45 cine-MRI volumes achieving an average F1-score of 77.83% on the task of binary classification of among fourth cardiac conditions.