  Convolutional neural networks are powerful tools for image segmentation and
classification. Here, we use this method to identify and mark the heart region
of Drosophila at different developmental stages in the cross-sectional images
acquired by a custom optical coherence microscopy (OCM) system. With our
well-trained convolutional neural network model, the heart regions through
multiple heartbeat cycles can be marked with an intersection over union (IOU)
of ~86%. Various morphological and dynamical cardiac parameters can be
quantified accurately with automatically segmented heart regions. This study
demonstrates an efficient heart segmentation method to analyze OCM images of
the beating heart in Drosophila.
