A deep learning approach for fully automated cardiac shape modeling in tetralogy of Fallot.

Journal: Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
PMID: 36849960

Abstract

BACKGROUND: Cardiac shape modeling is a useful computational tool that has provided quantitative insights into the mechanisms underlying dysfunction in heart disease. The manual input and time required to make cardiac shape models, however, limits their clinical utility. Here we present an end-to-end pipeline that uses deep learning for automated view classification, slice selection, phase selection, anatomical landmark localization, and myocardial image segmentation for the automated generation of three-dimensional, biventricular shape models. With this approach, we aim to make cardiac shape modeling a more robust and broadly applicable tool that has processing times consistent with clinical workflows.

Authors

  • Sachin Govil
    Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, MC 0412, La Jolla, CA, 92093-0412, USA.
  • Brendan T Crabb
    Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA.
  • Yu Deng
    National Engineering Research Center for Cereal Fermentation and Food Biomanufacturing, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, People's Republic of China.
  • Laura Dal Toso
    School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
  • Esther Puyol-Anton
  • Kuberan Pushparajah
    School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
  • Sanjeet Hegde
    Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
  • James C Perry
    Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
  • Jeffrey H Omens
    Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, MC 0412, La Jolla, CA, 92093-0412, USA.
  • Albert Hsiao
    Department of Radiology, University of California, San Diego, 9452 Medical Center Dr, 4th Floor, La Jolla, CA 92037 (T.A.R., S.J.K., K.E.J., A.C.Y., S.S.B., L.D.H., A.H.); and Department of Mathematics and Statistics, San Diego State University, San Diego, Calif (K.A.H.).
  • Alistair A Young
    Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand.
  • Andrew D McCulloch
    Institute for Engineering in Medicine, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0412, USA.

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