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

• 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
• 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

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.

项目总结

项目成果

A Majorization-Minimization Algorithm for Neuroimage Registration.

神经图像配准的专业化最小化算法。

• DOI: 10.1137/22m1516907
• 发表时间: 2024
• 期刊: SIAM journal on imaging sciences
• 影响因子: 2.1
• 作者: Zhou,Gaiting;Tward,Daniel;Lange,Kenneth
• 通讯作者: Lange,Kenneth