  HOLODEC, an airborne cloud particle imager, captures holographic images of a
fixed volume of cloud to characterize the types and sizes of cloud particles,
such as water droplets and ice crystals. Cloud particle properties include
position, diameter, and shape. We present a hologram processing algorithm,
HolodecML, that utilizes a neural segmentation model, GPUs, and computational
parallelization. HolodecML is trained using synthetically generated holograms
based on a model of the instrument, and predicts masks around particles found
within reconstructed images. From these masks, the position and size of the
detected particles can be characterized in three dimensions. In order to
successfully process real holograms, we find we must apply a series of image
corrupting transformations and noise to the synthetic images used in training.
  In this evaluation, HolodecML had comparable position and size estimation
performance to the standard processing method, but improved particle detection
by nearly 20\% on several thousand manually labeled HOLODEC images. However,
the improvement only occurred when image corruption was performed on the
simulated images during training, thereby mimicking non-ideal conditions in the
actual probe. The trained model also learned to differentiate artifacts and
other impurities in the HOLODEC images from the particles, even though no such
objects were present in the training data set, while the standard processing
method struggled to separate particles from artifacts. The novelty of the
training approach, which leveraged noise as a means for parameterizing
non-ideal aspects of the HOLODEC detector, could be applied in other domains
where the theoretical model is incapable of fully describing the real-world
operation of the instrument and accurate truth data required for supervised
learning cannot be obtained from real-world observations.
