ArticleMolecular biology of the cell2014
Extracting meaning from biological imaging data.
Article in Molecular biology of the cell, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- LEVERSC: Cross-Platform Scriptable Multichannel 3-D Visualization for Fluorescence Microscopy Images.Frontiers in bioinformatics · 2022Article
- A massively multi-scale approach to characterizing tissue architecture by synchrotron micro-CT applied to the human placenta.Journal of the Royal Society, Interface · 2021Article
- Macrophages of diverse phenotypes drive vascularization of engineered tissues.Science advances · 2020Article
- Separating Touching Cells Using Pixel Replicated Elliptical Shape Models.IEEE transactions on medical imaging · 2019Article
- Machine learning applications in cell image analysis.Immunology and cell biology · 2017Review
- A Critical and Comparative Review of Fluorescent Tools for Live-Cell Imaging.Annual review of physiology · 2017Review
- LEVER: software tools for segmentation, tracking and lineaging of proliferating cells.Bioinformatics (Oxford, England) · 2016Article
- A Practical Approach to Quantitative Processing and Analysis of Small Biological Structures by Fluorescent Imaging.Journal of biomolecular techniques : JBT · 2016Article
- Site-specific recombinatorics: in situ cellular barcoding with the Cre Lox system.BMC systems biology · 2016Article
Corrections and comments
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Authors and funding
1 author.
Funding
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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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.