title: Challenges of cardiac image analysis in large-scale population-based studies.
abstract: Large-scale population-based imaging studies of preclinical and clinical heart disease are becoming possible due to the advent of standardized robust non-invasive imaging methods and infrastructure for big data analysis. This gives an exciting opportunity to gain new information about the development and progression of heart disease across population groups. However, the large amount of image data and prohibitive time required for image analysis present challenges for obtaining useful derived data from the images. Automated analysis tools for cardiac image analysis are only now becoming available. This paper reviews the challenges and possible solutions to the analysis of big imaging data in population studies. We also highlight the potential of recent large epidemiological studies using cardiac imaging to discover new knowledge on heart health and well-being.
authors: Department of Anatomy with Radiology, Faculty of Medical and Health Sciences, University of Auckland, 85 Park Road, Auckland, 1142, New Zealand, p.medrano@auckland.ac.nz.
authors: Pau
authors: P
authors: Medrano-Gracia
authors: Brett R
authors: BR
authors: Cowan
authors: Avan
authors: A
authors: Suinesiaputra
authors: Alistair A
authors: AA
authors: Young
mesh_terms: D057791:Cardiac Imaging Techniques / Q000379:methods*; D002318:Cardiovascular Diseases / Q000175:diagnosis*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D008953:Models, Anatomic; D055414:Myocardial Perfusion Imaging / Q000379:methods
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