Masateru Kawakubo1, Daichi Moriyama2,3, Yuzo Yamasaki4, Kohtaro Abe5, Kazuya Hosokawa5, Tetsuhiro Moriyama6, Pandji Triadyaksa7, Adi Wibowo8, Michinobu Nagao9, Hideo Arai10, Hiroshi Nishimura10, Toshiaki Kadokami10. 1. Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka-shi, Fukuoka, 812-8582, Japan. kawakubo.masateru.968@m.kyushu-u.ac.jp. 2. Department of Health Sciences, School of Medical Sciences, Kyushu University, Fukuoka, Japan. 3. Department of Radiological Technology, Hiroshima City Hiroshima Citizens Hospital, Hiroshima, Japan. 4. Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan. 5. Department of Cardiovascular Medicine, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan. 6. Institute of Mathematics for Industry, Kyushu University, Fukuoka, Japan. 7. Department of Physics, Faculty of Science and Mathematics, Universitas Diponegoro, Semarang, Indonesia. 8. Department of Computer Science, Faculty of Science and Mathematics, Universitas Diponegoro, Semarang, Indonesia. 9. Department of Diagnostic Imaging and Nuclear Medicine, Tokyo Women's Medical University, Tokyo, Japan. 10. Fukuokaken Saiseikai, Futsukaichi Hospital, Fukuoka, Japan.
Abstract
OBJECTIVE: We propose a deep learning-based fully automatic right ventricle (RV) segmentation technique that targets radially reconstructed long-axis (RLA) images of the center of the RV region in routine short axis (SA) cardiovascular magnetic resonance (CMR) images. Accordingly, the purpose of this study is to compare the accuracy of deep learning-based fully automatic segmentation of RLA images with the accuracy of conventional deep learning-based segmentation in SA orientation in terms of the measurements of RV strain parameters. MATERIALS AND METHODS: We compared the accuracies of the above-mentioned methods in RV segmentations and in measuring RV strain parameters by Dice similarity coefficients (DSCs) and correlation coefficients. RESULTS: DSC of RV segmentation of the RLA method exhibited a higher value than those of the conventional SA methods (0.84 vs. 0.61). Correlation coefficient with respect to manual RV strain measurements in the fully automatic RLA were superior to those in SA measurements (0.5-0.7 vs. 0.1-0.2). DISCUSSION: Our proposed RLA realizes accurate fully automatic extraction of the entire RV region from an available CMR cine image without any additional imaging. Our findings overcome the complexity of image analysis in CMR without the limitations of the RV visualization in echocardiography.
OBJECTIVE: We propose a deep learning-based fully automatic right ventricle (RV) segmentation technique that targets radially reconstructed long-axis (RLA) images of the center of the RV region in routine short axis (SA) cardiovascular magnetic resonance (CMR) images. Accordingly, the purpose of this study is to compare the accuracy of deep learning-based fully automatic segmentation of RLA images with the accuracy of conventional deep learning-based segmentation in SA orientation in terms of the measurements of RV strain parameters. MATERIALS AND METHODS: We compared the accuracies of the above-mentioned methods in RV segmentations and in measuring RV strain parameters by Dice similarity coefficients (DSCs) and correlation coefficients. RESULTS: DSC of RV segmentation of the RLA method exhibited a higher value than those of the conventional SA methods (0.84 vs. 0.61). Correlation coefficient with respect to manual RV strain measurements in the fully automatic RLA were superior to those in SA measurements (0.5-0.7 vs. 0.1-0.2). DISCUSSION: Our proposed RLA realizes accurate fully automatic extraction of the entire RV region from an available CMR cine image without any additional imaging. Our findings overcome the complexity of image analysis in CMR without the limitations of the RV visualization in echocardiography.
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