NeuRegenerate: A Framework for Visualizing Neurodegeneration
NeuRegenerate: A Framework for Visualizing Neurodegeneration
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DOI：
10.1109/tvcg.2021.3127132
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发表时间：
2021-11
影响因子：
5.2
通讯作者：
S. Boorboor;Shawn Mathew;M. Ananth;D. Talmage;L. Role;A. Kaufman
中科院分区：
文献类型：
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作者：
S. Boorboor;Shawn Mathew;M. Ananth;D. Talmage;L. Role;A. Kaufman
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，由于生物样本只能在单个时间点成像的限制，研究神经投射随时间的变化仅限于使用群体分析收集的观察结果。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指定年龄-时间点内的神经纤维形态变化。为了预测预测，我们提出了neuReGANerator，这是一种基于周期一致性生成对抗网络的深度学习网络，它可以将神经元结构的特征转换为大型脑显微镜体积的年龄时间点。我们通过实现密度倍增器和一个新的损失函数（称为幻觉损失）来提高预测神经元结构的重建质量。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。最后，为了可视化使用neuReGANerator预测的投影变化，NeuRegenerate提供了两种模式：（i）neuroCompare，从两个年龄域（使用结构视图和有界视图）同时可视化神经元投影结构的差异，以及（ii）neuroMorph，一种基于血管的变形技术，以交互方式可视化从一个年龄时间点到另一个年龄时间点的结构转换。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内的胆碱能系统的小鼠大脑之间的年轻和年老的标本。
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 paper, 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 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.