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
De Marvao A
(2015)
Adverse changes in left ventricular structure begin at normotensive systolic blood pressures: a high resolution MRI study
in Journal of Cardiovascular Magnetic Resonance
Duan J
(2019)
Automatic 3D Bi-Ventricular Segmentation of Cardiac Images by a Shape-Refined Multi- Task Deep Learning Approach.
in IEEE transactions on medical imaging
Oktay O
(2017)
Stratified Decision Forests for Accurate Anatomical Landmark Localization in Cardiac Images
in IEEE Transactions on Medical Imaging
Peressutti D
(2017)
Registration of Multiview Echocardiography Sequences Using a Subspace Error Metric
in IEEE Transactions on Biomedical Engineering
Peressutti D
(2017)
A framework for combining a motion atlas with non-motion information to learn clinically useful biomarkers: Application to cardiac resynchronisation therapy response prediction.
in Medical image analysis
Schafer S
(2016)
Titin-truncating variants affect heart function in disease cohorts and the general population
in Nature Genetics
Sinclair M
(2018)
Myocardial strain computed at multiple spatial scales from tagged magnetic resonance imaging: Estimating cardiac biomarkers for CRT patients.
in Medical image analysis
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 |