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A Computational Framework for Distributed Registration of Massive Neuroscience Images

海量神经科学图像分布式配准的计算框架

基本信息

批准号：
10259930
负责人：
Matthew McCormick
金额：
$ 136.52万
依托单位：
KITWARE, INC.
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-15 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10259930
关键词：
Adopted Adoption Anatomy Architecture BRAIN initiative Brain Brain Mapping Brain imaging Brain region Cells Clinical Communities Computing Methodologies Data Data Set Development Documentation Electron Microscopy Electrophysiology (science)Ensure Fluorescence Microscopy Genetic Goals Histopathology Image Image Analysis Infiltration Institutes Laboratory Research Letters Libraries Light Magnetic Resonance Imaging Manuals Maps Measurement Medical Memory Methods Modality Modernization NPAS4 gene Neurons Neurosciences Neurosciences Research Online Systems Ontology Performance Physiological Pythons Reproducibility Research Research Personnel Resolution Roentgen Rays Sampling Science Series Software Framework Structure Technology Time Tissues Toxoplasmosis Visualization Work analytical tool base bioimaging cluster computing computer framework data acquisition deep learning design experience experimental study image registration imaging modality imaging system improved insight member microCT microscopic imaging monocyte neuroimaging next generation novel open source optical imaging outreach prevent relating to nervous system terabyte tool transcriptomics

项目摘要

Project Summary Neuroscience stands at the precipice of a new depth of understanding about how the brain works thanks to recent advances in imaging data acquisition technologies such as light-sheet ﬂuorescence microscopy (LSFM). How- ever, the lack of analytic tooling to mine this rich information's relationship across samples, timepoints, and data acquisition technologies prevents researchers from unlocking quantitative relationships. We propose the creation of an easy-to-use, distributed-computation image registration tools that will map large images into a common reference frame. This work will be based on the open source Insight Toolkit (ITK), a widely supported, standard library for reproducible, computational image analysis. We propose extending ITK's registration architecture with technologies and methods from deep learning and the scientiﬁc Python community to effectively register LSFM volumes and time series. This project has the potential to integrate recent advances in cell typing and circuit mapping that will ultimately elucidate the underlying mechanisms of brain development and function.

项目摘要神经科学站在一个新的理解深度的悬崖上，由于最近的研究，成像数据采集技术的进步，如光片荧光显微镜（LSFM）。怎么-- 以往，缺乏分析工具来挖掘这些丰富的信息在样本、时间点和数据之间的关系获取技术阻碍了研究人员解开数量关系。我们建议创建一个易于使用的，分布式计算的图像配准工具，将大图像映射到一个共同的参照系这项工作将基于开源Insight Toolkit（ITK），这是一个广泛支持的标准，用于可再现的计算图像分析的库。我们建议扩展ITK的注册架构，深度学习和科学Python社区的技术和方法，以有效地注册LSFM 数量和时间序列。该项目有可能整合细胞分型和电路的最新进展这将最终阐明大脑发育和功能的潜在机制。