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

Evaluating integrative strategies for incorporating phenotypic features in spatial transcriptomics.

Journal of microscopy·2026
Same author

Identifying and targeting abnormal mitochondrial localization associated with psychosis.

bioRxiv : the preprint server for biology·2026
Same author

GloBIAS: strengthening the foundations of bioimage analysis.

Nature methods·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 journal

Mechanisms underpinning chromosome structure in metazoans.

Molecular biology of the cell·2026
Same journal

Conserved and Divergent Modes of Substrate Interaction Define Selective Localizations and Functions of a Cdc14 Phosphatase.

Molecular biology of the cell·2026
Same journal

Dimerization of the centriolin-like protein Nud1 governs spindle pole body inheritance in budding yeast.

Molecular biology of the cell·2026
Same journal

Non-muscle Myosin II acts as a negative feedback mediator to control cell contraction dynamics in adherent cells.

Molecular biology of the cell·2026
Same journal

The tetraspanin disc proteins, peripherin-2 and ROM1, facilitate CNG channel localization to the rod outer segment.

Molecular biology of the cell·2026
Same journal

Csf1 facilitates adaptive membrane lipid remodeling linked to ER-plasma membrane contact sites.

Molecular biology of the cell·2026
See all related articles

Related Experiment Video

Updated: Nov 8, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.1K

Open-source deep-learning software for bioimage segmentation.

Alice M Lucas1, Pearl V Ryder1, Bin Li2

  • 1Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, 02142.

Molecular Biology of the Cell
|April 19, 2021
PubMed
Summary
This summary is machine-generated.

Deep learning tools now make complex bioimage analysis accessible to biologists. Several new open-source software solutions simplify image segmentation, overcoming previous computational barriers.

More Related Videos

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

9.9K
Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

7.3K

Related Experiment Videos

Last Updated: Nov 8, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.1K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

9.9K
Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

7.3K

Area of Science:

  • Biology
  • Bioimage Analysis
  • Computational Biology

Background:

  • Microscopy images contain vital information on biological structures.
  • Extracting this information is challenging due to complex structures, texture-based distinctions, and low signal-to-noise ratios.
  • Traditional bioimage analysis methods struggle with these complexities.

Purpose of the Study:

  • To survey accessible deep learning software tools for bioimage analysis.
  • To make deep learning-based image segmentation feasible for biologists with limited computational expertise.
  • To highlight advancements in making complex image analysis more approachable.

Main Methods:

  • Survey of open-source software tools for deep learning-based image segmentation.
  • Categorization of tools into web apps, software plug-ins, and interactive notebooks/pipelines.
  • Overview of current challenges in the field.

Main Results:

  • Several user-friendly, open-source deep learning tools are now available.
  • These tools reduce the computational expertise required for advanced bioimage analysis.
  • Diverse tool formats cater to different user needs and existing workflows.

Conclusions:

  • Deep learning is revolutionizing bioimage analysis by providing accessible tools.
  • Biologists can now leverage powerful image segmentation techniques without extensive computational training.
  • Continued development aims to address remaining challenges in the field.