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Related Experiment Video

Updated: Feb 19, 2026

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques

Published on: June 30, 2017

Quality Control for High-Throughput Imaging Experiments Using Machine Learning in Cellprofiler.

Mark-Anthony Bray^1,2, Anne E Carpenter³

¹Novartis Institutes for BioMedical Research, Cambridge, MA, USA.

Methods in Molecular Biology (Clifton, N.J.)

|October 31, 2017

Summary

This summary is machine-generated.

Automated microscopy requires robust quality control to remove image artifacts. This study presents an open-source protocol using CellProfiler and CellProfiler Analyst to enhance high-content screening accuracy.

Keywords:

Cell-based assays High-content screening Image analysis Machine learning Microscopy Open-source software Quality control

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A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

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Published on: March 1, 2017

Area of Science:

Cellular imaging and analysis
Bioimage informatics
High-content screening

Background:

Automated microscopy and quantitative image analysis enable high-content screening of cellular phenotypes.
Image-based aberrations like focus blur and saturation can contaminate screening data.
Accurate identification of subtle cellular phenotypes requires removal of these artifacts.

Purpose of the Study:

To develop an automated quality control protocol for high-content screening.
To leverage open-source software for image analysis and machine learning.
To improve the reliability of visual cellular phenotype identification.

Main Methods:

Implementation of an automated quality control protocol.
Utilizing image-based measurements from CellProfiler.
Employing machine-learning functionality within CellProfiler Analyst.

Main Results:

The protocol effectively identifies and removes common image-based aberrations.
Automated quality control enhances the accuracy of downstream analysis.
Subtle cellular phenotypes can be identified more reliably.

Conclusions:

An automated quality control protocol using open-source tools improves high-content screening.
This approach is critical for reliable identification of cellular phenotypes.
The protocol enhances the robustness of automated microscopy workflows.