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The exploitation of large-scale population data has the potential to improvehealthcare by discovering and understanding patterns and trends within thisdata. To enable high throughput analysis of cardiac imaging data automatically,a pipeline should comprise quality monitoring of the input images, segmentationof the cardiac structures, assessment of the segmentation quality, and parsingof cardiac functional indexes. We present a fully automatic, high throughputimage parsing workflow for the analysis of cardiac MR images, and test itsperformance on the UK Biobank (UKB) cardiac dataset. The proposed pipeline iscapable of performing end-to-end image processing including: data organisation,image quality assessment, shape model initialisation, segmentation,segmentation quality assessment, and functional parameter computation; allwithout any user interaction. To the best of our knowledge,this is the firstpaper tackling the fully automatic 3D analysis of the UKB population study,providing reference ranges for all key cardiovascular functional indexes, fromboth left and right ventricles of the heart. We tested our workflow on areference cohort of 800 healthy subjects for which manual delineations, andreference functional indexes exist. Our results show statistically significantagreement between the manually obtained reference indexes, and thoseautomatically computed using our framework.

High Throughput Computation of Reference Ranges of Biventricular Cardiac Function on the UK Biobank Population Cohort