3D Reconstruction and Analysis of Alzheimer's Patient Biopsy Samples to Map and Quantify Hallmarks of Pathogenesis and Vulnerability

项目摘要/摘要 这一竞争性补充申请需要资金来推进标准化的工具和方法 将大规模3D EM图像体积作为增强性能、可重用性和可重复性的手段 自动卷的高通量人工智能和机器学习(AI/ML)算法 脑细胞和亚细胞超微结构的分割。这项工作将在 积极的研究项目，正在推动获取、处理/提炼和传播大规模 3D EM参考数据来自患者遗留的脑组织活检样本 患有阿尔茨海默病(AD)(5R01AG065549)。这个活跃的项目深深植根于使用 先进的AI/ML技术用于描绘神经元和神经胶质细胞的关键超微结构成分 公元前十年发展的标志。它的组织是为了全面瞄准与斑块相关的区域， 缠结和脑血管系统，关注现有发现提示细胞和网络的位置 易受攻击性，并包含一些人怀疑的分子相互作用，这些相互作用是 广告。通过这项工作，我们正在推进经过充分训练的神经网络的发展和传播。 用于体积分割的模型，以简化(并降低)社区提取工作的成本 他们自己的3D几何图形和来自AD参考数据集合的相关形态测量数据以及类似数据 神经元3D EM数据的储存库。通过这一补充努力，我们将制定、完善和传播一套 允许直接反馈和标准化初级图像质量的工具，从而基准可以 建立以整体优化整个过程，为形象提供更严格的定义目标 图像采集时的特征。这项工作的结果是促进了迁移学习的使用 方法，促进用于可缩放EM图像的训练的神经网络模型的重复性和重用性 分段。

PROJECT SUMMARY/ABSTRACT This competitive supplement application requests funds to advance tools and methodology for the normalization of large-scale, 3D EM image volumes as a means to enhance the performance, reusability, and repeatability of high throughput artificial intelligence and machine learning (AI/ML) algorithms for automatic volume segmentation of brain cellular and subcellular ultrastructure. This work will be conducted in the context of an active research project that is advancing the acquisition, processing/refinement, and dissemination of large-scale 3D EM reference data derived from a remarkable collection of legacy biopsy brain samples from patients suffering from Alzheimer’s Disease (AD) (5R01AG065549). This active project is deeply rooted in the use of advance AI/ML technologies for delineating key ultrastructural constituents of neurons and glia exhibiting hallmarks of the progression of AD. It is organized to comprehensively target areas associated with plaques, tangles and brain vasculature, attending to locations where existing findings suggest cell and network vulnerability and contain molecular interactions suspected by some to underlie the initiation and progression of AD. Through this work, we are advancing the development and dissemination of fully trained neural-network models for volume segmentation to simplify (and reduce the costs associated with) community efforts to extract their own 3D geometries and associated morphometrics from this collection of AD reference data and similar repositories of neuronal 3D EM data. With this supplemental effort, we will develop, refine and disseminate a set of tools which allow for direct feedback and standardization of primary image quality, whereby benchmarks can be established so as to optimize the entire process holistically, giving a more rigorously defined target for image characteristics at time of image acquisition. The outcome of this work is to advance the use of transfer learning methods, facilitating repeatability and reuse of trained neural network models for scalable EM image segmentation.