no code implementations • 2 Feb 2022 • Saeed Boorboor, Shawn Mathew, Mala Ananth, David Talmage, Lorna W. Role, Arie E. Kaufman
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.