  Recent advances in high-resolution microscopy have allowed scientists to
better understand the underlying brain connectivity. However, due to the
limitation that biological specimens can only be imaged at a single timepoint,
studying changes to neural projections is limited to general observations using
population analysis. In this paper, we introduce NeuRegenerate, a novel
end-to-end framework for the prediction and visualization of changes in neural
fiber morphology within a subject, for specified age-timepoints.To predict
projections, we present neuReGANerator, a deep-learning network based on
cycle-consistent generative adversarial network (cycleGAN) that translates
features of neuronal structures in a region, across age-timepoints, for large
brain microscopy volumes. We improve the reconstruction quality of neuronal
structures by implementing a density multiplier and a new loss function, called
the hallucination loss.Moreover, to alleviate artifacts that occur due to
tiling of large input volumes, we introduce a spatial-consistency module in the
training pipeline of neuReGANerator. We show that neuReGANerator has a
reconstruction accuracy of 94% in predicting neuronal structures. Finally, to
visualize the predicted change in projections, NeuRegenerate offers two modes:
(1) neuroCompare to simultaneously visualize the difference in the structures
of the neuronal projections, across the age timepoints, and (2) neuroMorph, a
vesselness-based morphing technique to interactively visualize the
transformation of the structures from one age-timepoint to the other. Our
framework is designed specifically for volumes acquired using wide-field
microscopy. We demonstrate our framework by visualizing the structural changes
in neuronal fibers within the cholinergic system of the mouse brain between a
young and old specimen.
