  Cardiac image segmentation is a powerful tool in regard to diagnostics and
treatment of cardiovascular diseases. Purely feature-based detection of
anatomical structures like the mitral valve is a laborious task due to
specifically required feature engineering and is especially challenging in
echocardiograms, because of their inherently low contrast and blurry boundaries
between some anatomical structures. With the publication of further annotated
medical datasets and the increase in GPU processing power, deep learning-based
methods in medical image segmentation became more feasible in the past years.
We propose a fully automatic detection method for mitral valve hinge points,
which uses a U-Net based deep neural net to segment cardiac chambers in
echocardiograms in a first step, and subsequently extracts the mitral valve
hinge points from the resulting segmentations in a second step. Results
measured with this automatic detection method were compared to reference
coordinate values, which with median absolute hinge point coordinate errors of
1.35 mm for the x- (15-85 percentile range: [0.3 mm; 3.15 mm]) and 0.75 mm for
the y- coordinate (15-85 percentile range: [0.15 mm; 1.88 mm]).
