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

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

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 e...

Full description

Bibliographic Details
Main Authors: Sachin Govil, Brendan T. Crabb, Yu Deng, Laura Dal Toso, Esther Puyol-Antón, Kuberan Pushparajah, Sanjeet Hegde, James C. Perry, Jeffrey H. Omens, Albert Hsiao, Alistair A. Young, Andrew D. McCulloch
Format: Article
Language:English
Published: BMC 2023-02-01
Series:Journal of Cardiovascular Magnetic Resonance
Subjects:
Cardiovascular magnetic resonance (CMR)
Image segmentation
Deep learning
Shape modeling
Congenital heart disease
Online Access:https://doi.org/10.1186/s12968-023-00924-1

Internet

https://doi.org/10.1186/s12968-023-00924-1

Similar Items