Toward a scalable framework for reproducible processing of volumetric, nanoscale neuroimaging datasets
| Autor: | Hannah P. Cowley, Corban G. Rivera, Konrad P. Kording, Erik C. B. Johnson, Miller Wilt, Jordan Matelsky, Brock A. Wester, Raphael Norman-Tenazas, Elizabeth P. Reilly, William Gray-Roncal, Luis M Rodriguez, Theodore J. LaGrow, Joseph Downs, Marisa Hughes, Eva L. Dyer, Nathan Drenkow, Dean M. Kleissas |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
microtomography
Gigabyte Computer science AcademicSubjects/SCI02254 Health Informatics Neuroimaging Machine learning computer.software_genre reproducible science Workflow 03 medical and health sciences 0302 clinical medicine Technical Note Ecosystem 030304 developmental biology 0303 health sciences Computational neuroscience electron microscopy business.industry Petabyte Computer Science Applications Computer data storage Scalability Connectome AcademicSubjects/SCI00960 containers workflows Artificial intelligence business computer optimization 030217 neurology & neurosurgery Algorithms Software computational neuroscience |
| Zdroj: | GigaScience |
| ISSN: | 2047-217X |
| Popis: | Background Emerging neuroimaging datasets (collected with imaging techniques such as electron microscopy, optical microscopy, or X-ray microtomography) describe the location and properties of neurons and their connections at unprecedented scale, promising new ways of understanding the brain. These modern imaging techniques used to interrogate the brain can quickly accumulate gigabytes to petabytes of structural brain imaging data. Unfortunately, many neuroscience laboratories lack the computational resources to work with datasets of this size: computer vision tools are often not portable or scalable, and there is considerable difficulty in reproducing results or extending methods. Results We developed an ecosystem of neuroimaging data analysis pipelines that use open-source algorithms to create standardized modules and end-to-end optimized approaches. As exemplars we apply our tools to estimate synapse-level connectomes from electron microscopy data and cell distributions from X-ray microtomography data. To facilitate scientific discovery, we propose a generalized processing framework, which connects and extends existing open-source projects to provide large-scale data storage, reproducible algorithms, and workflow execution engines. Conclusions Our accessible methods and pipelines demonstrate that approaches across multiple neuroimaging experiments can be standardized and applied to diverse datasets. The techniques developed are demonstrated on neuroimaging datasets but may be applied to similar problems in other domains. |
| Databáze: | OpenAIRE |
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