Augmented serial blockface histology: Toward a better understanding of 3D tissue microstructure

增强串行块面组织学：更好地理解 3D 组织微观结构

• 批准号: RGPIN-2020-06109
• 负责人: Lefebvre, Joël
• 金额: $ 1.75万
• 依托单位: Université du Québec à Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=757304
• 关键词: Augmented; serial; blockface; histology; Toward

Over the last decades, technological advances in imaging assisted the scientific community for linking functions to structures inside the brain. Neuroimaging modalities such as magnetic resonance imaging (MRI) and positron emission tomography (PET) have revolutionized our understanding of the brain. These tools have been used to study, among other investigations, brain metabolism and neurodegenerative diseases. Despite the ubiquitous use of MRI and PET imaging in neuroscience, these techniques are not well suited when it comes to study the brain structure at a micrometer scale. In the field of neurophotonics, a tool that meets this increasing resolution requirement is serial blockface histology (SBH). This technology combines a tissue slicing apparatus with an optical microscope. The sample is sequentially sliced to reveal new tissue layers that are imaged with the microscope. The process is repeated until the whole sample has been imaged. Then, through advanced registration methods, the thousands of image tiles acquired are assembled into a single 3D volume. SBH was an essential component of many high-profile neuroscience projects mapping genome-wide gene expression and for obtaining a micrometre scale connectome in a whole mouse brain. One of the main challenges is that SBH generates a tremendous amount of data for every brain. For example, to acquire an entire mouse brain with a 40X objective offering sampling resolution of 1 µm would require an estimated acquisition time of 60 days with current SBH systems and would necessitate around 700 Terabytes (TB) of disk space to store the raw dataset. This represents a challenge for data management, reconstruction, and the analysis methods. Future applications in this neurophotonics field will increasingly require a closer synergy between imaging and machine learning. My Discovery research program has two objectives: (1) to accelerate image acquisition and reconstruction by integrating the microscope with advanced computer vision methods, and (2) to analyze the large amount of data generated by such imaging systems with machine learning based methods. The projects proposed in this research program aim to create an augmented serial blockface histology system by integrating the microscopy with novel computer vision methods, image processing and machine learning techniques. This will make this imaging technique faster, smarter and more reproducible. This will open the way to democratization of SBH and is a necessary step toward the broader adoption of this technology by the biomedical research and clinical communities.

在过去的几十年中，成像的技术进步有助于科学界将功能与大脑内部的结构联系起来。神经影像模式（例如磁共振成像（MRI）和正电子发射断层扫描（PET））彻底改变了我们对大脑的理解。这些工具已用于研究脑代谢和神经退行性疾病。尽管在神经科学中使用MRI和PET成像无处不在，但这些技术在以千分尺尺度研究大脑结构时并不适合。在神经素化学领域，满足这种增加的分辨率要求的工具是串行区块组织学（SBH）。这项技术将组织切片设备与光学显微镜相结合。样品被依次切成薄片，以揭示与显微镜成像的新组织层。重复该过程，直到整个样品成像为止。然后，通过高级注册方法，将所采集的数千个图像图块组装成单个3D卷。 SBH是许多众多备受瞩目的神经科学项目的重要组成部分，绘制了全基因组基因表达，并且用于在整个小鼠大脑中获得微米量表连接组。主要挑战之一是SBH为每个大脑生成大量数据。例如，要获得40倍物镜的整个小鼠大脑，提供1 µm的采样分辨率将需要使用当前的SBH系统的估计获取时间为60天，并且需要大约700 TBYTES（TB）的磁盘空间来存储RAW数据集。这代表了数据管理，重建和分析方法的挑战。在这个神经传播领域中的未来应用将增加需求的成像和机器学习之间的紧密协同作用。我的发现研究计划有两个目标：（1）通过将显微镜与先进的计算机视觉方法整合在一起，以加速图像采集和重建，（2）分析具有基于机器学习方法的成像系统生成的大量数据。该研究计划中提出的项目旨在通过将显微镜与新颖的计算机视觉方法，图像处理和机器学习技术整合在一起来创建增强的串行区块组织学系统。这将使这种成像技术更快，更聪明，更可重复。这将为SBH民主化开辟道路，这是生物医学研究和临床社区更广泛采用这项技术的必要步骤。