Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Identifying and targeting abnormal mitochondrial localization associated with psychosis.

bioRxiv : the preprint server for biology·2026
Same author

Progress and new challenges in image-based profiling.

Molecular systems biology·2026
Same author

Cell Painting for cytotoxicity and mode-of-action analysis in primary human hepatocytes.

Cell systems·2026
Same author

AI agents in drug discovery: applications and case studies.

Drug discovery today·2026
Same author

A scalable approach to resolving variants of uncertain significance.

bioRxiv : the preprint server for biology·2026
Same author

Counting cells can accurately predict small-molecule bioactivity benchmarks.

Nature communications·2026

Related Experiment Video

Updated: May 3, 2026

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

12.8K

EXTRACTING BIOMEDICALLY IMPORTANT INFORMATION FROM LARGE, AUTOMATED IMAGING EXPERIMENTS.

Anne E Carpenter1

  • 1Imaging Platform, Broad Institute of Harvard and MIT.

Proceedings. IEEE International Symposium on Biomedical Imaging
|February 15, 2014
PubMed
Summary
This summary is machine-generated.

Extracting information from high-throughput microscopy is challenging. Ongoing research addresses segmenting cells, tracking them over time, quantifying phenotypes, and finding cell subpopulations.

Keywords:
C. elegansco-culturesfluorescence microscopyhigh-throughputscreening

More Related Videos

High-throughput Imaging and Analysis Workflow for Evaluating Skin Cell Phenotypes and Proliferation States in Tissue Samples
11:24

High-throughput Imaging and Analysis Workflow for Evaluating Skin Cell Phenotypes and Proliferation States in Tissue Samples

Published on: October 31, 2025

895
Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
08:40

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

12.4K

Related Experiment Videos

Last Updated: May 3, 2026

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

12.8K
High-throughput Imaging and Analysis Workflow for Evaluating Skin Cell Phenotypes and Proliferation States in Tissue Samples
11:24

High-throughput Imaging and Analysis Workflow for Evaluating Skin Cell Phenotypes and Proliferation States in Tissue Samples

Published on: October 31, 2025

895
Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
08:40

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

12.4K

Area of Science:

  • Bioimage analysis
  • High-throughput microscopy

Background:

  • High-throughput microscopy generates vast datasets, but extracting meaningful biological information remains difficult.
  • Current methods struggle with complex segmentation, tracking, and analysis tasks.

Purpose of the Study:

  • To outline key challenges in high-throughput microscopy data analysis.
  • To highlight areas of active research in automated image analysis for biological discovery.

Main Methods:

  • Segmentation of diverse biological structures (neurons, co-cultures, organisms).
  • Segmentation and tracking of cells in time-lapse microscopy.
  • Quantification of complex cellular phenotypes.
  • Computational methods for discovering cell subpopulations.

Main Results:

  • Identified critical bottlenecks in current bioimage analysis pipelines.
  • Demonstrated the need for advanced algorithms for accurate cell segmentation and tracking.
  • Highlighted the complexity of quantifying phenotypic changes from microscopy data.
  • Emphasized the potential of data-driven approaches for identifying novel cell populations.

Conclusions:

  • Significant advancements are needed in image analysis algorithms for high-throughput microscopy.
  • Automated and robust methods are crucial for unlocking the full potential of microscopy data.
  • Future research should focus on integrated approaches for segmentation, tracking, quantification, and subpopulation discovery.