NeuRegenerate: A Framework for Visualizing Neurodegeneration

• Published in: IEEE transactions on visualization and computer graphics 2023-03, Vol.29 (3), p.1625-1637
• Main Authors: Boorboor, Saeed, Mathew, Shawn, Ananth, Mala, Talmage, David, Role, Lorna W., Kaufman, Arie E.
• Format: Article
• Language: English
• Subjects: Age; Animals; Brain; Brain - diagnostic imaging; Cholinergics; Computer Graphics; Data visualization; Generative adversarial networks; Hallucinations; Head; Image Processing, Computer-Assisted - methods; Image reconstruction; machine learning; Mice; Microscopy; Morphing; Neurites; Neuron visualization; Three-dimensional displays; Tiling; Training; Visualization; volume transformation; volume visualization; wide-field microscopy
• ISSN: 1077-2626; 1941-0506
• DOI: 10.1109/TVCG.2021.3127132

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 over time is limited to observations gathered using population analysis. In this article, we introduce NeuRegenerate , a novel end-to-end framework for the prediction and visualization of changes in neural fiber morphology within a subject across specified age-timepoints. To predict projections, we present neuReGANerator , a deep-learning network based on cycle-consistent generative adversarial network (GAN) that translates features of neuronal structures across age-timepoints for large brain microscopy volumes. We improve the reconstruction quality of the predicted 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. Finally, to visualize the change in projections, predicted using neuReGANerator, NeuRegenerate offers two modes: (i) neuroCompare to simultaneously visualize the difference in the structures of the neuronal projections, from two age domains (using structural view and bounded view), and (ii) 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 within the cholinergic system of the mouse brain between a young and old specimen.