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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

Crossref DOI link: https://doi.org/10.1007/s10334-022-01017-3

Published Online: 2022-05-18

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Kawakubo, Masateru https://orcid.org/0000-0003-1867-1745
Moriyama, Daichi

Yamasaki, Yuzo

Abe, Kohtaro

Hosokawa, Kazuya

Moriyama, Tetsuhiro

Triadyaksa, Pandji

Wibowo, Adi

Nagao, Michinobu

Arai, Hideo

Nishimura, Hiroshi

Kadokami, Toshiaki
Funding

Funding for this research was provided by:

Japan Society for the Promotion of Science (JP20K16729)
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Text and Data Mining valid from 2022-05-18

Version of Record valid from 2022-05-18
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Article History

Received: 1 February 2022

Revised: 26 March 2022

Accepted: 25 April 2022

First Online: 18 May 2022

Declarations

:

: This retrospective study was approved by the institutional review board in Kyushu University and Saiseikai Futsukaichi Hospital and conducted in accordance with the 1964 Declaration of Helsinki.

: The review board waived the requirement for written informed consent. The manuscript does not contain any individual person’s data in any form.

: Not applicable.

: This study has a pending patent (2020–181621).

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