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:
- 金额:$ 136.52万
- 依托单位:
- 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:
- 关键词:AdoptedAdoptionAnatomyArchitectureBRAIN initiativeBrainBrain MappingBrain imagingBrain regionCellsClinicalCommunitiesComputing MethodologiesDataData SetDevelopmentDocumentationElectron MicroscopyElectrophysiology (science)EnsureFluorescence MicroscopyGeneticGoalsHistopathologyImageImage AnalysisInfiltrationInstitutesLaboratory ResearchLettersLibrariesLightMagnetic Resonance ImagingManualsMapsMeasurementMedicalMemoryMethodsModalityModernizationNPAS4 geneNeuronsNeurosciencesNeurosciences ResearchOnline SystemsOntologyPerformancePhysiologicalPythonsReproducibilityResearchResearch PersonnelResolutionRoentgen RaysSamplingScienceSeriesSoftware FrameworkStructureTechnologyTimeTissuesToxoplasmosisVisualizationWorkanalytical toolbasebioimagingcluster computingcomputer frameworkdata acquisitiondeep learningdesignexperienceexperimental studyimage registrationimaging modalityimaging systemimprovedinsightmembermicroCTmicroscopic imagingmonocyteneuroimagingnext generationnovelopen sourceoptical imagingoutreachpreventrelating to nervous systemterabytetooltranscriptomics
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。
项目成果
期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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:
- 影响因子:2.1
- 作者:Zhou,Gaiting;Tward,Daniel;Lange,Kenneth
- 通讯作者:Lange,Kenneth
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