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Statistical learning methods in deep cardiac phenotyping for population imaging and imaging genetics

  • Full or part time
  • Application Deadline
    Monday, April 01, 2019
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

Co-supervisors: Professor Sven Plein (School of Medicine, University of Leeds), Dr Tanveer Syeda-Mahmood (IBM Fellow & Chief Scientist), IBM Research and Dr Enzo Ferrante (Scientist) CONICET, Universidad Nacional del Litoral.

Cardiovascular conditions like heart failure, coronary artery disease and structural heart disease manifest in alterations of the anatomy or deformation of the myocardium. Cardiovascular magnetic resonance (CMR) is increasingly recognised as the most accurate test for the non-invasive characterisation of the myocardium and provides a plethora of methods for detailed phenotyping.

Many heart conditions have a genetic basis but their expression in cardiac functional phenotypes is highly variable. For example, family members of patients with inherited heart disease are often genotype positive but phenotype negative – when currently available testing methods and analyses are used. More sophisticated phenotyping methods will combine usefully with genetic testing. The aim of this study is to develop fully automatic cardiac image analysis methods providing detailed three-dimensional phenotyping of cardiac morphology and deformation, scaling up to hundreds and thousands of datasets in population imaging studies, and statistical methods that relate quantitative image phenotypes with genetics and omics data. The expected outcome is an improved understanding of their interplay and the development of more sensitive and specific patient stratification schemes and risk scores.

This project will explore three main avenues:
a) will undertake a detailed comparison of competing algorithms for CMR analysis applicable to population imaging studies and will use UK Biobank as testbed to carry out large-scale scalability tests;
b) will develop new machine learning techniques for CMR analysis (e.g. methods exploiting available metadata and producing measures of uncertainty on their outputs); and
c) will explore statistical methods for imaging genetics applied to CMR and accounting for uncertainty on the image-derived phenotypes.

Funding Notes

Funding will be awarded on a competitive basis.

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