Large-scale investigation of deep learning approaches for ventilated lung segmentation using multi-nuclear hyperpolarized gas MRI

Astley, J.R., Biancardi, A.M., Hughes, P.J.C. orcid.org/0000-0002-7979-5840 et al. (8 more authors) (2022) Large-scale investigation of deep learning approaches for ventilated lung segmentation using multi-nuclear hyperpolarized gas MRI. Scientific Reports, 12 (1). 10566. ISSN 2045-2322

Abstract

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Copyright, Publisher and Additional Information: © The Author(s) 2022. Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Keywords: Magnetic resonance imaging; Respiratory tract diseases
Dates:
  • Accepted: 10 June 2022
  • Published (online): 22 June 2022
  • Published: 22 June 2022
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Sheffield Teaching Hospitals
Funding Information:
FunderGrant number
MEDICAL RESEARCH COUNCILMR/M008894/1
NIHR AcademyNIHR-RP-R3-12-027
Depositing User: Symplectic Sheffield
Date Deposited: 30 Jun 2022 11:32
Last Modified: 03 Jul 2022 16:51
Status: Published
Publisher: Nature Publishing Group
Refereed: Yes
Identification Number: https://doi.org/10.1038/s41598-022-14672-2
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