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
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
Bai W
(2015)
Multi-atlas segmentation with augmented features for cardiac MR images.
in Medical image analysis
Bernard O
(2016)
Standardized Evaluation System for Left Ventricular Segmentation Algorithms in 3D Echocardiography.
in IEEE transactions on medical imaging
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
De Marvao A
(2015)
Precursors of Hypertensive Heart Phenotype Develop in Healthy Adults: A High-Resolution 3D MRI Study.
in JACC. Cardiovascular imaging
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
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
Sohal M
(2014)
A prospective evaluation of cardiovascular magnetic resonance measures of dyssynchrony in the prediction of response to cardiac resynchronization therapy.
in Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Suinesiaputra A
(2018)
Statistical shape modeling of the left ventricle: myocardial infarct classification challenge.
in IEEE journal of biomedical and health informatics
Thanaj M
(2022)
Genetic and environmental determinants of diastolic heart function.
in Nature cardiovascular research
Xi J
(2014)
Understanding the need of ventricular pressure for the estimation of diastolic biomarkers.
in Biomechanics and modeling in mechanobiology
Zhuang X
(2015)
Multiatlas whole heart segmentation of CT data using conditional entropy for atlas ranking and selection.
in Medical physics
| 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 |