Three-Dimensional Segmentation of the Left Ventricle in Late Gadolinium Enhanced MR Images of Chronic Infarction Combining Long- and Short-Axis Information

TL;DR

This paper introduces a comprehensive 3D segmentation framework for the left ventricle in late gadolinium enhanced cardiac MRI images, effectively handling intensity heterogeneity and leveraging both long- and short-axis information.

Contribution

The novel framework combines a priori cine image contours with a 3D deformation model and a parametric LV model to improve segmentation accuracy in challenging LGE images.

Findings

Accurate segmentation demonstrated on real patient data

Robustness to variations in initial contours and pathological conditions

Consistent performance across different imaging orientations

Abstract

Automatic segmentation of the left ventricle (LV) in late gadolinium enhanced (LGE) cardiac MR (CMR) images is difficult due to the intensity heterogeneity arising from accumulation of contrast agent in infarcted myocardium. In this paper, we present a comprehensive framework for automatic 3D segmentation of the LV in LGE CMR images. Given myocardial contours in cine images as a priori knowledge, the framework initially propagates the a priori segmentation from cine to LGE images via 2D translational registration. Two meshes representing respectively endocardial and epicardial surfaces are then constructed with the propagated contours. After construction, the two meshes are deformed towards the myocardial edge points detected in both short-axis and long-axis LGE images in a unified 3D coordinate system. Taking into account the intensity characteristics of the LV in LGE images, we propose a novel parametric model of the LV for consistent myocardial edge points detection regardless of pathological status of the myocardium (infarcted or healthy) and of the type of the LGE images (short-axis or long-axis). We have evaluated the proposed framework with 21 sets of real patient and 4 sets of simulated phantom data. Both distance- and region-based performance metrics confirm the observation that the framework can generate accurate and reliable results for myocardial segmentation of LGE images. We have also tested the robustness of the framework with respect to varied a priori segmentation in both practical and simulated settings. Experimental results show that the proposed framework can greatly compensate variations in the given a priori knowledge and consistently produce accurate segmentations.