Space and time shape constrained deformable surfaces for 4D medical image segmentation

Abstract

The aim of this work is to automatically extract quantitative parameters from time sequences of 3D images (4D images) suited to heart pathology diagnosis. In this paper, we propose a framework for the reconstruction of the left ventricle motion from 4D images based on 4D deformable surface models. These 4D models are represented as a time sequence of 3D meshes whose deformation are correlated during the cardiac cycle. Both temporal and spatial constraints based on prior knowledge of heart shape and motion are combined to improve the segmentation accuracy. In contrast to many earlier approaches, our framework includes the notion of trajectory constraint. We have demonstrated the ability of this segmentation tool to deal with noisy or low contrast images on 4D MR, SPECT, and US images.