3D deep convolutional neural network-based ventilated lung segmentation using multi-nuclear hyperpolarized gas MRI

Astley, J., Biancardi, A., Hughes, P. et al. (8 more authors) (2020) 3D deep convolutional neural network-based ventilated lung segmentation using multi-nuclear hyperpolarized gas MRI. In: Petersen, J., Estépar, R.S.J., Schmidt-Richberg, A., Gerard, S., Lassen-Schmidt, B., Jacobs, C., Beichel, R. and Mori, K., (eds.) Thoracic Image Analysis. TIA 2020, 08 Oct 2020, Lima, Peru. Lecture Notes in Computer Science , 12502 . Springer Verlag , pp. 24-35. ISBN 9783030624682

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Copyright, Publisher and Additional Information: © 2020 Springer Nature Switzerland AG. This is an author-produced version of a paper subsequently published in Petersen J. et al. (eds) Thoracic Image Analysis. TIA 2020. Lecture Notes in Computer Science, vol 12502. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: Functional lung imaging; Hyperpolarized gas MRI; Deep learning; Convolutional neural network; Lung segmentation
Dates:
  • Accepted: 4 November 2020
  • Published (online): 4 November 2020
  • Published: 4 November 2020
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Sheffield Teaching Hospitals
Depositing User: Symplectic Sheffield
Date Deposited: 16 Nov 2020 16:07
Last Modified: 16 Nov 2020 16:07
Status: Published
Publisher: Springer Verlag
Series Name: Lecture Notes in Computer Science
Refereed: Yes
Identification Number: https://doi.org/10.1007/978-3-030-62469-9_3

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