  Cardiovascular magnetic resonance (CMR) imaging is a standard imaging
modality for assessing cardiovascular diseases (CVDs), the leading cause of
death globally. CMR enables accurate quantification of the cardiac chamber
volume, ejection fraction and myocardial mass, providing information for
diagnosis and monitoring of CVDs. However, for years, clinicians have been
relying on manual approaches for CMR image analysis, which is time consuming
and prone to subjective errors. It is a major clinical challenge to
automatically derive quantitative and clinically relevant information from CMR
images. Deep neural networks have shown a great potential in image pattern
recognition and segmentation for a variety of tasks. Here we demonstrate an
automated analysis method for CMR images, which is based on a fully
convolutional network (FCN). The network is trained and evaluated on a
large-scale dataset from the UK Biobank, consisting of 4,875 subjects with
93,500 pixelwise annotated images. The performance of the method has been
evaluated using a number of technical metrics, including the Dice metric, mean
contour distance and Hausdorff distance, as well as clinically relevant
measures, including left ventricle (LV) end-diastolic volume (LVEDV) and
end-systolic volume (LVESV), LV mass (LVM); right ventricle (RV) end-diastolic
volume (RVEDV) and end-systolic volume (RVESV). By combining FCN with a
large-scale annotated dataset, the proposed automated method achieves a high
performance on par with human experts in segmenting the LV and RV on short-axis
CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR
images.
