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

阿尔茨海默病患者活检样本的 3D 重建和分析，以绘制和量化发病机制和脆弱性的标志

• 批准号: 10594236
• 负责人: Mark H Ellisman
• 金额: $ 31.6万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10594236
• 关键词: 3-Dimensional; Advanced Development; Alzheimer' s Disease; Alzheimer' s disease patient; Area; Artificial Intelligence; Benchmarking; Binding; Biopsy; Biopsy Specimen; Brain; Cells; Cerebrovascular system; Characteristics; Collection; Communities; Data; Data Set; Electrons; Exhibits; Feedback; Funding; Geometry; Image; Location; Machine Learning; Maps; Methodology; Microscopic; Neural Network Simulation; Neurofibrillary Tangles; Neuroglia; Neurons; Noise; Outcome; Pathogenesis; Patients; Performance; Plant Roots; Process; Request for Applications; Research Project Grants; Resolution; Sampling; Signal Transduction; Standardization; Technology; Testing; Time; Training; Work; automated algorithm; cost; image processing; imaging Segmentation; learning strategy; machine learning algorithm; reconstruction; repository; targeted imaging; tool; transfer learning

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.

项目总结/摘要 这一竞争性补充申请要求提供资金，以推进标准化的工具和方法 作为一种手段，以提高性能，可重用性和可重复性， 高吞吐量人工智能和机器学习（AI/ML）算法，用于自动卷取 脑细胞和亚细胞超微结构的分割。这项工作将在一个 一个积极的研究项目，正在推进收购，处理/完善，和大规模的传播 3D EM参考数据来源于患者遗留活检脑样本的显著收集 患有阿尔茨海默病（AD）（5 R 01 AG 065549）。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， AD进展的标志。它的组织是全面针对与斑块相关的区域， 缠结和脑血管系统，关注现有发现表明细胞和网络的位置 脆弱性和包含分子相互作用怀疑一些基础的启动和发展， AD.通过这项工作，我们正在推进经过充分训练的神经网络的开发和传播。 用于卷分割的模型，以简化（并降低相关成本）社区的工作， 他们自己的3D几何形状和相关的形态测量，从这个收集的AD参考数据和类似的 神经元3D EM数据库。通过这一补充努力，我们将制定、完善和传播一套 允许直接反馈和主要图像质量标准化的工具， 建立以全面优化整个流程，为图像提供更严格定义的目标 图像采集时的特征。这项工作的成果是促进迁移学习的使用 方法，促进可扩展EM图像的训练神经网络模型的可重复性和重用 细分