Automatic quantification of epicardial fat volume on non‐enhanced cardiac CT scans using a multi‐atlas segmentation approach

Abstract

The authors developed a fully automatic method that is capable of segmenting the pericardium and quantifying epicardial fat on non-enhanced cardiac CT scans. The authors demonstrated the feasibility of using this method to replace manual annotations by showing that the automatic method performs as good as manual annotation on a large dataset.

Automated pericardium delineation and epicardial fat volume quantification from noncontrast CT

“…This method also requires an expert observer to scroll the slices between the atrioventricular sulcus and the apex and to place control points on the pericardium. Shahzad et al 35 proposed an automated epicardial fat quantification method using a multiatlas segmentation approach, 36 similar to the atlas-based initialization part in our method. The authors registered an atlas created with CTA data to the noncontrast test CT scans to segment the pericardium.…”

Automatic pericardium segmentation and quantification of epicardial fat from computed tomography angiography

“…Recently, a few methods have been developed for automated pericardium segmentation. Shahzad et al 3 use multi-atlas segmentation with majority voting. Practically, the same method was applied for cardiac segmentation by Kirisli and Schaap.…”

“…Demographics of these subjects are shown in Table 1. The images have resolutions ranging between 512 × 512 × 342 and 512 × 512 × 458 voxels with voxel dimensions between 0.32 × 0.32 × 0.30 mm 3 and 0.43 × 0.43 × 0.30 mm 3 . The study was approved by the ethics committee at Umeå University and adheres to the Declaration of Helsinki.…”

“…In medical applications, the registration methods are typically intensity-based and nonrigid, e.g., as in Ref. 3, which tend to be computationally very demanding. As our intention is to apply our framework to thousands of images, a more efficient method is required.…”

Automated quantification of epicardial adipose tissue in cardiac magnetic resonance imaging

“…However, this process is not only time consuming, but also subject to interobserver variability. Several approaches have been published in CT images [6], [7], but no attempt to quantification in CMR has been published. The main reason is the difficulty associated to the localization of the pericardium in CMR images, since usually it is not clearly visible (it is hardly visible as a very thin line, blurred due to partial volume effect).…”