Segmentation of Left Ventricle from 3D Cardiac MR Image Sequences Using a Subject-Specific Dynamical Model

Abstract

Statistical model-based segmentation of the left ventricle from cardiac images has received considerable attention in recent years. While a variety of statistical models have been shown to improve segmentation results, most of them are either static models (SM) which neglect the temporal coherence of a cardiac sequence or generic dynamical models (GDM) which neglect the inter-subject variability of cardiac shapes and deformations. In this paper, we use a subject-specific dynamical model (SSDM) that handles inter-subject variability and temporal dynamics (intra-subject variability) simultaneously. It can progressively identify the specific motion patterns of a new cardiac sequence based on the segmentations observed in the past frames. We formulate the integration of the SSDM into the segmentation process in a recursive Bayesian framework in order to segment each frame based on the intensity information from the current frame and the prediction from the past frames. We perform "Leave-one-out" test on 32 sequences to validate our approach. Quantitative analysis of experimental results shows that the segmentation with the SSDM outperforms those with the SM and GDM by having better global and local consistencies with the manual segmentation.