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Columbus Image Data Storage and Analysis System

Our Columbus Image Data Storage and Analysis system is an instrument agnostic image analysis and management platform.

NEW! Signals Image Artist is our next-generation solution for image analysis and management.

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Powerful image analysis capabilities with highly flexible and easy to use building blocks to analyze simple and complex phenotypes of cells.
Real-time image analysis utilizing cluster based high performance computing (HPC) with Columbus Building Blocks.
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Image storage and analysis for all your high content imaging data.

High Content Screening experiments generate massive amounts of image data that needs to be accessed quickly, analyzed and re-analyzed, shared with colleagues and stored safely. With the trend towards using more complex, physiologically relevant disease models, more sophisticated tools are required to numerically describe cells and their phenotypes comprehensively. The Columbus system is the only image data storage and analysis system that supports a wide range of file formats, allowing visualization of images, regardless of their origin.

A powerful new way to access, store and explore

  • Import images from any major high content imaging instrument for a single solution for data storage and analysis.
  • Access, visualize, and analyze image data from the convenience of your web browser. Access the data not just from within a single lab but from across the enterprise.
  • Store all image data in a central location, with associated metadata to give a complete and enduring picture of any experiment.
  • Get started without extensive training – browse and explore your data using the intuitive user interface.
  • Utilize pre-designed segmentation routines and feature extraction algorithms, such as STAR morphology, to perform sophisticated analysis with ease.

Image Analysis for Phenotypic Screening

  • Generate statistically-significant, quantitative and multi-parametric data from your cell images for robust results.
  • Includes powerful image analysis building blocks that are easy to use and have been designed to be used by researchers, not just image analysis experts
  • Measure complex and subtle phenotypic responses by extracting the properties that describe the unique cellular fingerprint
  • Compare multiple samples, plates or batches to quality check your results
  • Relate results to concentrations with dose response curves
  • Kinetic measurement capabilities enable you to measure time-resolved data and track cells to reveal changes in cell properties and provide information on cell movement
  • Analyze new images or re-analyze images from past experiments using our building blocks which encapsulate our many years of experience in high content analysis
  • PhenoLOGIC machine-learning technology allows you to easily create customized algorithms using a simple point-and-click approach
  • Access your Columbus results directly in Signals Screening via the High Content Profiler app and perform screening data analysis and validation, QC analyses, calculate reliable normalization, perform multivariate hit stratification and drug response profiling.
  • Columbus 2.9.1 supports MuviCyte.


Version Number Powerful image analysis capabilities with highly flexible and easy to use building blocks to analyze simple and complex phenotypes of cells.
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Case Study

Phenotypic Characterization of Mitochondria in Breast Cancer Cells using Morphology and Texture Properties

This study illustrates the power of high content automated image analysis to quantify complex organelle morphologies, using the Opera system and the texture and STAR features of the Columbus system.


White Paper

Artificial Intelligence, Machine Learning and Deep Learning: Applications in Cellular Imaging for Improved Drug Discovery Productivity

There has been a lot of buzz around artificial intelligence, machine learning and deep learning. Is the reality living up to the hype?

In the world of cellular imaging and its application to drug discovery, there is evidence of real progress against some of the critical challenges facing scientists using these technologies.

In this white paper, you will learn about:

  • Challenges in cellular imaging and drug discovery that Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) are helping to overcome
  • How these technologies are used by leading cellular imaging scientists
  • An outlook to how AI, ML and DL in cellular imaging have the potential to further advance drug discovery and improve productivity in the future

High Content Screening in Three Dimensions

Researchers are increasingly looking to 3D cell cultures, microtissues, and organoids to bridge the gap between 2D cell cultures and in vivo animal models. This whitepaper documents a streamlined procedure for getting the most information, as quickly as possible, using solutions from PerkinElmer.