  The exploitation of large-scale population data has the potential to improve
healthcare by discovering and understanding patterns and trends within this
data. To enable high throughput analysis of cardiac imaging data automatically,
a pipeline should comprise quality monitoring of the input images, segmentation
of the cardiac structures, assessment of the segmentation quality, and parsing
of cardiac functional indexes. We present a fully automatic, high throughput
image parsing workflow for the analysis of cardiac MR images, and test its
performance on the UK Biobank (UKB) cardiac dataset. The proposed pipeline is
capable of performing end-to-end image processing including: data organisation,
image quality assessment, shape model initialisation, segmentation,
segmentation quality assessment, and functional parameter computation; all
without any user interaction. To the best of our knowledge,this is the first
paper tackling the fully automatic 3D analysis of the UKB population study,
providing reference ranges for all key cardiovascular functional indexes, from
both left and right ventricles of the heart. We tested our workflow on a
reference cohort of 800 healthy subjects for which manual delineations, and
reference functional indexes exist. Our results show statistically significant
agreement between the manually obtained reference indexes, and those
automatically computed using our framework.
