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In the era of big data in scientific research, there is a necessity toleverage techniques which reduce human effort in labeling and categorizinglarge datasets by involving sophisticated machine tools. To combat thisproblem, we present a novel, general purpose model for 3D segmentation thatleverages patch-wise adversariality and Long Short-Term Memory to encodesequential information. Using this model alongside citizen science projectswhich use 3D datasets (image cubes) on the Zooniverse platforms, we propose aniterative human-machine optimization framework where only a fraction of the 2Dslices from these cubes are seen by the volunteers. We leverage the patch-wisediscriminator in our model to provide an estimate of which slices within theseimage cubes have poorly generalized feature representations, andcorrespondingly poor machine performance. These images with correspondingmachine proposals would be presented to volunteers on Zooniverse forcorrection, leading to a drastic reduction in the volunteer effort on citizenscience projects. We trained our model on ~2300 liver tissue 3D electronmicrographs. Lipid droplets were segmented within these images through humanannotation via the `Etch A Cell - Fat Checker' citizen science project, hostedon the Zooniverse platform. In this work, we demonstrate this framework and theselection methodology which resulted in a measured reduction in volunteereffort by more than 60%. We envision this type of joint human-machinepartnership will be of great use on future Zooniverse projects.

TCuPGAN: A novel framework developed for optimizing human-machine interactions in citizen science