Right ventricular strain and volume analyses through deep learning-based fully automatic segmentation based on radial long-axis reconstruction of short-axis cine magnetic resonance images.

Journal: Magma (New York, N.Y.)
Published Date: May 18, 2022

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.

Authors

  • Masateru Kawakubo
    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.
  • Daichi Moriyama
    Department of Health Sciences, School of Medical Sciences, Kyushu University, Fukuoka, Japan.
  • Yuzo Yamasaki
    Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi Ward, Fukuoka, 812-8582, Japan.
  • Kohtaro Abe
    Department of Cardiovascular Medicine, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
  • Kazuya Hosokawa
    Department of Cardiovascular Medicine, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
  • Tetsuhiro Moriyama
    Institute of Mathematics for Industry, Kyushu University, Fukuoka, Japan.
  • Pandji Triadyaksa
    Department of Physics, Faculty of Science and Mathematics, Universitas Diponegoro, Semarang, Indonesia.
  • Adi Wibowo
    Department of Computer Science, Faculty of Science and Mathematics, Universitas Diponegoro, Semarang, Indonesia.
  • Michinobu Nagao
    Department of Diagnostic Imaging and Nuclear Medicine, Tokyo Women's Medical University, Tokyo, Japan.
  • Hideo Arai
    Fukuokaken Saiseikai, Futsukaichi Hospital, Fukuoka, Japan.
  • Hiroshi Nishimura
    Fukuokaken Saiseikai, Futsukaichi Hospital, Fukuoka, Japan.
  • Toshiaki Kadokami
    Fukuokaken Saiseikai, Futsukaichi Hospital, Fukuoka, Japan.

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