Automated segmentation of 3D cine cardiovascular magnetic resonance imaging

Tayebi Arasteh S, Romanowicz J, Pace DF, Golland P, Powell AJ, Maier A, Truhn D, Brosch T, Weese J, Lotfinia M, van der Geest RJ, Moghari MH (2023)

Publication Language: English

Publication Type: Journal article, Original article

Publication year: 2023

Journal

Frontiers in Cardiovascular Medicine Frontiers Media

Book Volume: 10

DOI: 10.3389/fcvm.2023.1167500

Open Access Link: https://doi.org/10.3389/fcvm.2023.1167500

Abstract

Introduction: As the life expectancy of children with congenital heart disease (CHD) is rapidly increasing and the adult population with CHD is growing, there is an unmet need to improve clinical workflow and efficiency of analysis. Cardiovascular magnetic resonance (CMR) is a noninvasive imaging modality for monitoring patients with CHD. CMR exam is based on multiple breath-hold 2-dimensional (2D) cine acquisitions that should be precisely prescribed and is expert and institution dependent. Moreover, 2D cine images have relatively thick slices, which does not allow for isotropic delineation of ventricular structures. Thus, development of an isotropic 3D cine acquisition and automatic segmentation method is worthwhile to make CMR workflow straightforward and efficient, as the present work aims to establish. 

Methods: Ninety-nine patients with many types of CHD were imaged using a non-angulated 3D cine CMR sequence covering the whole-heart and great vessels. Automatic supervised and semi-supervised deep-learning-based methods were developed for whole-heart segmentation of 3D cine images to separately delineate the cardiac structures, including both atria, both ventricles, aorta, pulmonary arteries, and superior and inferior vena cavae. The segmentation results derived from the two methods were compared with the manual segmentation in terms of Dice score, a degree of overlap agreement, and atrial and ventricular volume measurements. 
Results: The semi-supervised method resulted in a better overlap agreement with the manual segmentation than the supervised method for all 8 structures (Dice score 83.23 ± 16.76% vs. 77.98 ± 19.64%; P-value ≤0.001). The mean difference error in atrial and ventricular volumetric measurements between manual segmentation and semi-supervised method was lower (bias ≤ 5.2 ml) than the supervised method (bias ≤ 10.1 ml). 
Discussion: The proposed semi-supervised method is capable of cardiac segmentation and chamber volume quantification in a CHD population with wide anatomical variability. It accurately delineates the heart chambers and great vessels and can be used to accurately calculate ventricular and atrial volumes throughout the cardiac cycle. Such a segmentation method can reduce inter- and intra- observer variability and make CMR exams more standardized and efficient.

Authors with CRIS profile

Soroosh Tayebi Arasteh Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

University of Colorado Anschutz Medical Campus US United States (USA) (US) Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen DE Germany (DE) Leiden University Medical Center NL Netherlands (NL) Universitätsklinikum Aachen (UKA) DE Germany (DE) Philips Deutschland GmbH DE Germany (DE) Massachusetts General Hospital US United States (USA) (US) Massachusetts Institute of Technology (MIT) US United States (USA) (US) Boston Children's Hospital US United States (USA) (US)

How to cite

APA:

Tayebi Arasteh, S., Romanowicz, J., Pace, D.F., Golland, P., Powell, A.J., Maier, A.,... Moghari, M.H. (2023). Automated segmentation of 3D cine cardiovascular magnetic resonance imaging. Frontiers in Cardiovascular Medicine, 10. https://dx.doi.org/10.3389/fcvm.2023.1167500

MLA:

Tayebi Arasteh, Soroosh, et al. "Automated segmentation of 3D cine cardiovascular magnetic resonance imaging." Frontiers in Cardiovascular Medicine 10 (2023).

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