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Published, Version of Record (VoR)CC BY-NC-SA V4.0, Open
Journal article
Extracting meaning from biological imaging data
Molecular biology of the cell, v 25(22), pp 3470-3473
05 Nov 2014
PMID: 25368423
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Biological imaging continues to improve, capturing continually longer-term, richer, and more complex data, penetrating deeper into live tissue. How do we gain insight into the dynamic processes of disease and development from terabytes of multidimensional image data? Here I describe a collaborative approach to extracting meaning from biological imaging data. The collaboration consists of teams of biologists and engineers working together. Custom computational tools are built to best exploit application-specific knowledge in order to visualize and analyze large and complex data sets. The image data are summarized, extracting and modeling the features that capture the objects and relationships in the data. The summarization is validated, the results visualized, and errors corrected as needed. Finally, the customized analysis and visualization tools together with the image data and the summarization results are shared. This Perspective provides a brief guide to the mathematical ideas that rigorously quantify the notion of extracting meaning from biological image, and to the practical approaches that have been used to apply these ideas to a wide range of applications in cell and tissue optical imaging.
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Details
- Title
- Extracting meaning from biological imaging data
- Creators
- Andrew R Cohen - Drexel University
- Publication Details
- Molecular biology of the cell, v 25(22), pp 3470-3473
- Grant note
- R01NS076709 / NINDS NIH HHS R01 NS076709 / NINDS NIH HHS R01AG040080 / NIA NIH HHS R01 AG041861 / NIA NIH HHS
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000344236800009
- Scopus ID
- 2-s2.0-84908583870
- Other Identifier
- 991019168824704721
UN Sustainable Development Goals (SDGs)
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- Web of Science research areas
- Cell Biology