  Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia,
characterised by a rapid and irregular electrical activation of the atria.
Treatments for AF are often ineffective and few atrial biomarkers exist to
automatically characterise atrial function and aid in treatment selection for
AF. Clinical metrics of left atrial (LA) function, such as ejection fraction
(EF) and active atrial contraction ejection fraction (aEF), are promising, but
have until now typically relied on volume estimations extrapolated from
single-slice images. In this work, we study volumetric functional biomarkers of
the LA using a fully automatic SEGmentation of the left Atrium based on a
convolutional neural Network (SEGANet). SEGANet was trained using a dedicated
data augmentation scheme to segment the LA, across all cardiac phases, in short
axis dynamic (CINE) Magnetic Resonance Images (MRI) acquired with full cardiac
coverage. Using the automatic segmentations, we plotted volumetric time curves
for the LA and estimated LA EF and aEF automatically. The proposed method
yields high quality segmentations that compare well with manual segmentations
(Dice scores [$0.93 \pm 0.04$], median contour [$0.75 \pm 0.31$] mm and
Hausdorff distances [$4.59 \pm 2.06$] mm). LA EF and aEF are also in agreement
with literature values and are significantly higher in AF patients than in
healthy volunteers. Our work opens up the possibility of automatically
estimating LA volumes and functional biomarkers from multi-slice CINE MRI,
bypassing the limitations of current single-slice methods and improving the
characterisation of atrial function in AF patients.
