Assessment of Bi-Ventricular and Bi-Atrial Areas Using Four-Chamber Cine Cardiovascular Magnetic Resonance Imaging: Fully Automated Segmentation with a U-Net Convolutional Neural Network

Arai, Hideo; Kawakubo, Masateru; Sanui, Kenichi; Iwamoto, Ryoji; Nishimura, Hiroshi; Kadokami, Toshiaki

doi:10.3390/ijerph19031401

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Assessment of Bi-Ventricular and Bi-Atrial Areas Using Four-Chamber Cine Cardiovascular Magnetic Resonance Imaging: Fully Automated Segmentation with a U-Net Convolutional Neural Network

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Fukuokaken Saiseikai Futsukaichi Hospital, Chikushino 818-8516, Japan

Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan

Author to whom correspondence should be addressed.

Academic Editor: Paul B. Tchounwou

Int. J. Environ. Res. Public Health 2022, 19(3), 1401; https://doi.org/10.3390/ijerph19031401

Received: 26 December 2021 / Revised: 18 January 2022 / Accepted: 26 January 2022 / Published: 27 January 2022

(This article belongs to the Special Issue In Multidisciplinary Perspectives: Healthcare Technology Research and Education Based on Artificial Intelligence and Simulation)

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Abstract

Four-chamber (4CH) cine cardiovascular magnetic resonance imaging (CMR) facilitates simultaneous evaluation of cardiac chambers; however, manual segmentation is time-consuming and subjective in practice. We evaluated deep learning based on a U-Net convolutional neural network (CNN) for fully automated segmentation of the four cardiac chambers using 4CH cine CMR. Cine CMR datasets from patients were randomly assigned for training (1400 images from 70 patients), validation (600 images from 30 patients), and testing (1000 images from 50 patients). We validated manual and automated segmentation based on the U-Net CNN using the dice similarity coefficient (DSC) and Spearman’s rank correlation coefficient (ρ); p < 0.05 was statistically significant. The overall median DSC showed high similarity (0.89). Automated segmentation correlated strongly with manual segmentation in all chambers—the left and right ventricles, and the left and right atria (end-diastolic area: ρ = 0.88, 0.76, 0.92, and 0.87; end-systolic area: ρ = 0.81, 0.81, 0.92, and 0.83, respectively; p < 0.01). The area under the curve for the left ventricle, left atrium, right ventricle, and right atrium showed high scores (0.96, 0.99, 0.88, and 0.96, respectively). Fully automated segmentation could facilitate simultaneous evaluation and detection of enlargement of the four cardiac chambers without any time-consuming analysis. View Full-Text

Keywords: cardiovascular magnetic resonance imaging; four-chamber cine imaging; fully automatic cardiac segmentation; heart chamber enlargement; convolutional neural network; U-Net

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Arai, H.; Kawakubo, M.; Sanui, K.; Iwamoto, R.; Nishimura, H.; Kadokami, T. Assessment of Bi-Ventricular and Bi-Atrial Areas Using Four-Chamber Cine Cardiovascular Magnetic Resonance Imaging: Fully Automated Segmentation with a U-Net Convolutional Neural Network. Int. J. Environ. Res. Public Health 2022, 19, 1401. https://doi.org/10.3390/ijerph19031401

AMA Style

Arai H, Kawakubo M, Sanui K, Iwamoto R, Nishimura H, Kadokami T. Assessment of Bi-Ventricular and Bi-Atrial Areas Using Four-Chamber Cine Cardiovascular Magnetic Resonance Imaging: Fully Automated Segmentation with a U-Net Convolutional Neural Network. International Journal of Environmental Research and Public Health. 2022; 19(3):1401. https://doi.org/10.3390/ijerph19031401

Chicago/Turabian Style

Arai, Hideo, Masateru Kawakubo, Kenichi Sanui, Ryoji Iwamoto, Hiroshi Nishimura, and Toshiaki Kadokami. 2022. "Assessment of Bi-Ventricular and Bi-Atrial Areas Using Four-Chamber Cine Cardiovascular Magnetic Resonance Imaging: Fully Automated Segmentation with a U-Net Convolutional Neural Network" International Journal of Environmental Research and Public Health 19, no. 3: 1401. https://doi.org/10.3390/ijerph19031401

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Assessment of Bi-Ventricular and Bi-Atrial Areas Using Four-Chamber Cine Cardiovascular Magnetic Resonance Imaging: Fully Automated Segmentation with a U-Net Convolutional Neural Network

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