A Comprehensive 3-D Framework for Automatic Quantification of Late Gadolinium Enhanced Cardiac Magnetic Resonance Images

TL;DR

This paper introduces a comprehensive 3-D framework for automatic quantification of LGE cardiac MRI images, improving segmentation and infarct classification accuracy despite image distortions and heterogeneity.

Contribution

It proposes a novel 3-D segmentation method using coupled mesh deformation and a graph-cut classification approach that incorporates spatial and intensity information.

Findings

Visually good segmentation and classification results.

Quantitative results strongly agree with expert manual annotations.

Effective correction of spatial and intensity distortions.

Abstract

Late gadolinium enhanced (LGE) cardiac magnetic resonance (CMR) can directly visualize nonviable myocardium with hyperenhanced intensities with respect to normal myocardium. For heart attack patients, it is crucial to facilitate the decision of appropriate therapy by analyzing and quantifying their LGE CMR images. To achieve accurate quantification, LGE CMR images need to be processed in two steps: segmentation of the myocardium followed by classification of infarcts within the segmented myocardium. However, automatic segmentation is difficult usually due to the intensity heterogeneity of the myocardium and intensity similarity between the infarcts and blood pool. Besides, the slices of an LGE CMR dataset often suffer from spatial and intensity distortions, causing further difficulties in segmentation and classification. In this paper, we present a comprehensive 3-D framework for automatic quantification of LGE CMR images. In this framework, myocardium is segmented with a novel method that deforms coupled endocardial and epicardial meshes and combines information in both short- and long-axis slices, while infarcts are classified with a graph-cut algorithm incorporating intensity and spatial information. Moreover, both spatial and intensity distortions are effectively corrected with specially designed countermeasures. Experiments with 20 sets of real patient data show visually good segmentation and classification results that are quantitatively in strong agreement with those manually obtained by experts.