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

Abstract

Recent findings indicate a strong correlation between the risk of future heart disease and the volume of adipose tissue inside of the pericardium. So far, large-scale studies have been hindered by the fact that manual delineation of the pericardium is extremely time-consuming and that existing methods for automatic delineation lack accuracy. An efficient and fully automatic approach to pericardium segmentation and epicardial fat volume (EFV) estimation is presented, based on a variant of multi-atlas segmentation for spatial initialization and a random forest classifier for accurate pericardium detection. Experimental validation on a set of 30 manually delineated computer tomography angiography volumes shows a significant improvement on state-of-the-art in terms of EFV estimation [mean absolute EFV difference: 3.8 ml (4.7%), Pearson correlation: 0.99] with run times suitable for large-scale studies (52 s). Further, the results compare favorably with interobserver variability measured on 10 volumes.

Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review

“…In the traditional analysis mode, radiologists have to delineate the boundary of EAT manually. This procedure is user-dependent, time-consuming, and poorly reproducible, so it is necessary to develop an accurate, quicker, and reproducible method for EAT segmentation (Norlén et al 2016 ; Commandeur et al 2018 ). Researchers have developed machine learning algorithms to segment EAT.…”

Deep Learning for Quantification of Epicardial and Thoracic Adipose Tissue From Non-Contrast CT

“…A detection of pericardium points was then performed along the direction of the sampling after a pre-processing step based on heart anatomy and intensity clustering. Recently, random forests were also proposed to classify voxels on the entire 3D pericardium shape [22]. Detection was performed along directions perpendicular to the pericardial surface, similarly to the radial sampling used in [19], which allows a rotational invariant feature detection, independently from the orientation with respect to the anatomy.…”

Epicardial and subcutenious adipose tissue adiponectin gene expression in coronary artery disease patients

“…Основные ЭхоКГ показатели оценивали фазированным матричным секторным датчиком M4S. «Золотым» стандартом оценки толщины ЭЖТ является компьютерная томография сердца и магнитно-резонансная томография [20,21]. В то же время эти исследования характеризуются трудоемкостью выполнения, необходимостью наличия специально обученного персонала, высокой стоимостью и, в случае компьютерной томографии, лучевой нагрузкой для пациента, что существенно ограничивает возможность их широкого применения в клинической практике для оценки выраженности эпикардиального ожирения [21].…”