Deep Learning Based Parametrization of Diffeomorphic Image Registration for the Application of Cardiac Image Segmentation

Cardiac segmentation from magnetic resonance imaging (MRI) is one of the essential tasks to analyze the anatomy and function of the heart for the assessment and diagnosis of cardiac diseases. However, manual annotation is difficult and time consuming. This study proposes a novel end-to-end supervised cardiac MRI segmentation framework based on a diffeomorphic deformable registration that can segment the left ventricle from 2D and 3D images or volumes. In order to represent the actual cardiac deformation, the methodology parameterizes the transformation using radial and rotational components, computed using a deep learning approach The method was evaluated over three different data sets and showed significant improvements compared to exacting learning and non-learning based methods in terms of the Dice score and Hausdorff distance metrics.