A Computational Framework for Distributed Registration of Massive Neuroscience Images

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。

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.