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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Organisations
Publications
Bernard O
(2016)
Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography.
in IEEE transactions on medical imaging
Suinesiaputra A
(2018)
Statistical shape modeling of the left ventricle: myocardial infarct classification challenge.
in IEEE journal of biomedical and health informatics
Oktay O
(2017)
Stratified Decision Forests for Accurate Anatomical Landmark Localization in Cardiac Images
in IEEE Transactions on Medical Imaging
Schafer S
(2016)
Titin-truncating variants affect heart function in disease cohorts and the general population
in Nature Genetics
Xi J
(2014)
Understanding the need of ventricular pressure for the estimation of diastolic biomarkers.
in Biomechanics and modeling in mechanobiology
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 | 09/2016 |
End | 09/2021 |