NeuRegenerate：一个可视化神经退行性变的框架
NeuRegenerate: A Framework for Visualizing Neurodegeneration
摘要：
高分辨率显微镜的最新进展使科学家能够更好地了解潜在的大脑连接。然而，由于生物标本只能在单个时间点成像的限制，研究神经投射的变化仅限于使用群体分析的一般观察。在本文中，我们介绍了NeuRegenerate，这是一种新型的端到端框架，用于预测和可视化受试者在特定年龄-时间点的神经纤维形态变化。为了预测投影，我们介绍了neuReGANerator，这是一种基于周期一致性生成对抗网络（cycleGAN）的深度学习网络，可以跨年龄-时间点转换区域中神经元结构的特征，用于大型脑显微镜体积。我们通过实现密度倍增器和一个新的损失函数来提高神经元结构的重建质量，称为幻觉损失。此外，为了减轻由于大输入体积的平铺而出现的伪影，我们在neuReGANerator的训练管道中引入了空间一致性模块。我们发现neuReGANerator在预测神经元结构方面的重建准确率为94%。最后，为了可视化预测的投影变化，NeuRegenerate提供了两种模式：（1）neuroCompare，可以同时可视化神经元投影结构的差异，跨越年龄时间点，（2）neuroMorph，一种基于血管的变形技术，可以交互式地可视化结构从一个年龄时间点到另一个年龄时间点的转换。我们的框架是专为使用宽视场显微镜获得的卷。我们展示了我们的框架，通过可视化的结构变化，神经纤维内的胆碱能系统的小鼠大脑之间的年轻和年老的标本。
英文摘要：
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.
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