  In the era of big data in scientific research, there is a necessity to
leverage techniques which reduce human effort in labeling and categorizing
large datasets by involving sophisticated machine tools. To combat this
problem, we present a novel, general purpose model for 3D segmentation that
leverages patch-wise adversariality and Long Short-Term Memory to encode
sequential information. Using this model alongside citizen science projects
which use 3D datasets (image cubes) on the Zooniverse platforms, we propose an
iterative human-machine optimization framework where only a fraction of the 2D
slices from these cubes are seen by the volunteers. We leverage the patch-wise
discriminator in our model to provide an estimate of which slices within these
image cubes have poorly generalized feature representations, and
correspondingly poor machine performance. These images with corresponding
machine proposals would be presented to volunteers on Zooniverse for
correction, leading to a drastic reduction in the volunteer effort on citizen
science projects. We trained our model on ~2300 liver tissue 3D electron
micrographs. Lipid droplets were segmented within these images through human
annotation via the `Etch A Cell - Fat Checker' citizen science project, hosted
on the Zooniverse platform. In this work, we demonstrate this framework and the
selection methodology which resulted in a measured reduction in volunteer
effort by more than 60%. We envision this type of joint human-machine
partnership will be of great use on future Zooniverse projects.
