학술논문
Automated segmentation of 3D cine cardiovascular magnetic resonance imaging.
Document Type
Academic Journal
Author
Tayebi Arasteh S; Department of Cardiology, Boston Children's Hospital, and Department of Pediatrics, Harvard Medical School, Boston, MA, United States.; Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.; Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany.; Romanowicz J; Department of Cardiology, Boston Children's Hospital, and Department of Pediatrics, Harvard Medical School, Boston, MA, United States.; Department of Cardiology, Children's Hospital Colorado, and School of Medicine, University of Colorado, Aurora, CO, United States.; Pace DF; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States.; Computer Science & Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, United States.; Golland P; Computer Science & Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, United States.; Powell AJ; Department of Cardiology, Boston Children's Hospital, and Department of Pediatrics, Harvard Medical School, Boston, MA, United States.; Maier AK; Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.; Truhn D; Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany.; Brosch T; Philips Research Laboratories, Hamburg, Germany.; Weese J; Philips Research Laboratories, Hamburg, Germany.; Lotfinia M; Institute of Heat and Mass Transfer, RWTH Aachen University, Aachen, Germany.; van der Geest RJ; Department of Radiology, Leiden University Medical Center, Leiden, Netherlands.; Moghari MH; Department of Radiology, Children's Hospital Colorado, and School of Medicine, University of Colorado, Aurora, CO, United States.
Source
Publisher: Frontiers Media S.A Country of Publication: Switzerland NLM ID: 101653388 Publication Model: eCollection Cited Medium: Print ISSN: 2297-055X (Print) Linking ISSN: 2297055X NLM ISO Abbreviation: Front Cardiovasc Med Subsets: PubMed not MEDLINE
Subject
Language
English
ISSN
2297-055X
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.
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(© 2023 Tayebi Arasteh, Romanowicz, Pace, Golland, Powell, Maier, Truhn, Brosch, Weese, Lotfinia, van der Geest and Moghari.)
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.
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(© 2023 Tayebi Arasteh, Romanowicz, Pace, Golland, Powell, Maier, Truhn, Brosch, Weese, Lotfinia, van der Geest and Moghari.)