Robust 3D Active Shape Model for the Segmentation of the Left Ventricle in MRI

Abstract

3D Active shape models use a set of annotated volumes to learn a shape model. The shape model is defined by a fixed number of landmarks at specific locations and takes shape constraints into account in the segmentation process. A relevant problem in which these models can be used is the segmentation of the left ventricle in 3D MRI volumes. In this problem, the annotations correspond to a set of contours that define the LV border at each volume slice. However, each volume has a different number of slices (i.e., a different number of landmarks), which makes model learning difficult. Furthermore, motion artifacts and the large distance between slices make interpolation of voxel intensities a bad choice when applying the learned model to a test volume. These two problems raise the following questions: (1) how can we learn a shape model from volumes with a variable number of slices? and (2) how can we segment a test volume without interpolating voxel intensities between slices? This paper provides an answer to these questions and proposes a 3D active shape model that can be used to segment the left ventricle in cardiac MRI.