Bi-temporal 3D active appearance models with applications to unsupervised ejection fraction estimation

Mikkel B. Stegmann; Dorthe Pedersen

doi:10.1117/12.594930

29 April 2005 Bi-temporal 3D active appearance models with applications to unsupervised ejection fraction estimation

Mikkel B. Stegmann, Dorthe Pedersen

Proceedings Volume 5747, Medical Imaging 2005: Image Processing; (2005) https://doi.org/10.1117/12.594930
Event: Medical Imaging, 2005, San Diego, California, United States

Abstract

Rapid and unsupervised quantitative analysis is of utmost importance to ensure clinical acceptance of many examinations using cardiac magnetic resonance imaging (MRI). We present a framework that aims at fulfilling these goals for the application of left ventricular ejection fraction estimation in four-dimensional MRI. The theoretical foundation of our work is the generative two-dimensional Active Appearance Models by Cootes et al., here extended to bi-temporal, three-dimensional models. Further issues treated include correction of respiratory induced slice displacements, systole detection, and a texture model pruning strategy. Cross-validation carried out on clinical-quality scans of twelve volunteers indicates that ejection fraction and cardiac blood pool volumes can be estimated automatically and rapidly with accuracy on par with typical inter-observer variability.

Citation Download Citation

Mikkel B. Stegmann and Dorthe Pedersen "Bi-temporal 3D active appearance models with applications to unsupervised ejection fraction estimation", Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); https://doi.org/10.1117/12.594930

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