Using Machine Learning to Identify Noninvasive Motion-Based Biomarkers of Cardiac Function

Lead Research Organisation: Imperial College London
Department Name: Computing

Abstract

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Publications

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Bai W (2016) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges

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Bai W (2015) A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion. in Medical image analysis

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Bai W (2015) Multi-atlas segmentation with augmented features for cardiac MR images. in Medical image analysis

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Bai W (2016) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges - 6th International Workshop, STACOM 2015, Held in Conjunction with MICCAI 2015, Munich, Germany, October 9, 2015, Revised Selected Papers

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Bai W (2015) Functional Imaging and Modeling of the Heart - 8th International Conference, FIMH 2015, Maastricht, The Netherlands, June 25-27, 2015. Proceedings

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Bernard O (2016) Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography. in IEEE transactions on medical imaging

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Corden B (2016) Relationship between body composition and left ventricular geometry using three dimensional cardiovascular magnetic resonance. in Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance

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Dawes TJW (2017) Machine Learning of Three-dimensional Right Ventricular Motion Enables Outcome Prediction in Pulmonary Hypertension: A Cardiac MR Imaging Study. in Radiology

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De Marvao A (2015) Precursors of Hypertensive Heart Phenotype Develop in Healthy Adults: A High-Resolution 3D MRI Study. in JACC. Cardiovascular imaging

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De Marvao A (2014) Population-based studies of myocardial hypertrophy: high resolution cardiovascular magnetic resonance atlases improve statistical power. in Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance

 
Description In this project we have developed a number of new machine learning techniques for the interpretation of cardiac MR images. These techniques can be used to automatically extract quantitative information about the cardiovascular system from MR images. In particular, we have developed algorithms for the automatic segmentation of the heart (left and right ventricle as well as myocardium) and the automated tracking of the heart. In addition, the project has developed techniques for the construction of an atlas of the structure and function of the heart.
Exploitation Route In future, the developed algorithms may be adopted by the medical imaging industry to support automatic interpretation of cardiac MR images.
Sectors Healthcare
URL https://biomedia.doc.ic.ac.uk/project/using-machine-learning-to-identify-noninvasive-motion-based-biomarkers-of-cardiac-function/
 
Description SmartHeart: Next-generation cardiovascular healthcare via integrated image acquisition, reconstruction, analysis and learning.
Amount £5,127,775 (GBP)
Funding ID EP/P001009/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 10/2016 
End 09/2021