Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

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

Infection with multiple strains of the Lyme disease bacterium in female mice can affect the strain-specific maternal antibody response in their offspring.

Ticks and tick-borne diseases·2026

Same author

Autophagy Is Required for Dopaminergic Axon Development and Confers Their Responsiveness to Guidance Cues.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2026

Same author

Enhancing STED microscopy via fluorescence lifetime unmixing and filtering in two-species SPLIT-STED.

Methods in microscopy·2026

Same author

Corrigendum to "Various small mammal hosts are involved in the transmission cycle of zoonotic Anaplasma phagocytophilum in Southeastern Canada" [Ticks and Tick-borne Diseases (2025) Volume 16, Issue 6].

Ticks and tick-borne diseases·2025

Same author

Early intervention for caregivers of youth with restrictive eating disorders (CARE Skills Group): feasibility, outcomes and opportunities for spread and scale.

Eating disorders·2025

Same author

Various small mammal hosts are involved in the transmission cycle of zoonotic Anaplasma phagocytophilum in Southeastern Canada.

Ticks and tick-borne diseases·2025

Same journal

Mapping the 3D Chromosome Organization of a Biosynthetic Gene Cluster by Capture Hi-C (CHi-C).

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Mapping the 3D Chromosome Organization of Streptomyces by Hi-C.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

CUT&Tag Epigenomic Profiling of Biosynthetic Gene Clusters in Arabidopsis thaliana.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Rhizobium rhizogenes-Mediated Hairy Root Transformation Protocol for Lotus japonicus and Other Legumes.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Characterization of Bioactive Saponins from Sea Cucumbers.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Methods for Functional Validation of Terpenoid Metabolic Clusters in Nicotiana benthamiana and Aspergillus oryzae.

Methods in molecular biology (Clifton, N.J.)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 2, 2025

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Automated Microscopy Image Segmentation and Analysis with Machine Learning.

Anthony Bilodeau^1,2, Catherine Bouchard^1,2, Flavie Lavoie-Cardinal^3,4

¹Université Laval, Québec, QC, Canada.

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

|February 26, 2022

Summary

This summary is machine-generated.

Automated image analysis pipelines using machine/deep learning (ML/DL) can extract quantitative information. This study outlines key steps and considerations for developing effective ML/DL image analysis pipelines, focusing on segmentation.

Keywords:

Deep learning Machine learning Microscopy Quantitative analysis Segmentation

More Related Videos

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

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

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

Published on: November 30, 2022

Related Experiment Videos

Last Updated: Oct 2, 2025

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

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

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

Published on: November 30, 2022

Area of Science:

Computational Biology
Medical Imaging
Artificial Intelligence

Background:

Quantitative image analysis is crucial for extracting meaningful data.
Traditional methods rely on predefined rules for parameter extraction, ensuring repeatability.
Machine/Deep Learning (ML/DL) offers automated rule extraction for image analysis tasks like segmentation, enumeration, and classification.

Purpose of the Study:

To present essential vocabulary for automated image analysis.
To detail the necessary steps for creating robust image segmentation pipelines.
To discuss critical technical considerations for developing ML/DL-driven automated image analysis.

Main Methods:

Literature review and synthesis of current practices in automated image analysis.
Conceptual framework development for ML/DL pipeline construction.
Identification and discussion of key parameters and technical aspects.

Main Results:

A structured overview of vocabulary relevant to automated image analysis.
A step-by-step guide for developing image segmentation pipelines.
Discussion of technical considerations for ML/DL implementation in image analysis.

Conclusions:

Developing effective automated image analysis pipelines requires careful planning and consideration of various parameters.
ML/DL techniques provide powerful tools for automating quantitative image analysis.
This work provides a foundational guide for researchers and developers in this field.