A Computational Framework for Distributed Registration of Massive Neuroscience Images

点击翻译按钮获取中文摘要

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.