Abstract
Significant technical advances in imaging, molecular biology and genomics have fueled a revolution in cell biology, in that the molecular and structural processes of the cell are now visualized and measured routinely. Driving much of this recent development has been the advent of computational tools for the acquisition, visualization, analysis and dissemination of these datasets. These tools collectively make up a new subfield of computational biology called bioimage informatics, which is facilitated by open source approaches. We discuss why open source tools for image informatics in cell biology are needed, some of the key general attributes of what make an open source imaging application successful, and point to opportunities for further operability that should greatly accelerate future cell biology discovery.
Highlights
Imaging is used as a tool for discovery throughout basic life science, and biomedical and clinical research
Driving much of this recent development has been the advent of computational tools for the acquisition, visualization, analysis and dissemination of these datasets. These tools collectively make up a new subfield of computational biology called bioimage informatics, which is facilitated by open source approaches
While still a relatively young field, bioimage informatics has already had a major impact in cell biology in the area of quantitative cell imaging where advanced feature recognition, segmentation, annotation and data mining approaches are used regularly [12,13,14,15,16,17,18,19,20]
Summary
Open source bioimage informatics for cell biology Swedlow, Jason R.; Eliceiri, Kevin W. Document Version Early version, known as pre-print Link to publication in Discovery Research Portal. Citation for published version (APA): Swedlow, J. Open source bioimage informatics for cell biology. Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim
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