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 Experiment Video

Updated: May 12, 2026

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

DP2: Distributed 3D image segmentation using micro-labor workforce.

Richard J Giuly1, Keun-Young Kim, Mark H Ellisman

  • 1National Center for Microscopy and Imaging Research, Center for Research in Biological Systems, Department of Neurosciences, University of California, San Diego, La Jolla, CA 92093, USA. rgiuly@ucsd.edu

Bioinformatics (Oxford, England)
|April 12, 2013
PubMed
Summary
This summary is machine-generated.

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

Disruption to TFEB signaling and autophagy in newly formed oligodendrocytes leads to aberrant generation of CNS myelin.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Therapeutic targeting of fibrin-microglia interactions ameliorates Alzheimer's disease-related hyperexcitability and brain network dysfunction.

bioRxiv : the preprint server for biology·2026
Same author

Resilience to neuronal hyperactivity and restoration of the neuroimmune interactome by blocking fibrin-induced microglia activation in Alzheimers disease.

bioRxiv : the preprint server for biology·2026
Same author

Impact of Resident Doctors' Strike on Psychological Outcomes Among Paramedics in Teaching-Hospital Emergency Departments: A Nationwide Multicenter Survey.

Healthcare (Basel, Switzerland)·2026
Same author

Adam9-deficient retinal pigment epithelium pseudopods maintain photoreceptor outer segment renewal despite subretinal space expansion.

The Journal of clinical investigation·2026
Same author

ER remodelling is a feature of ageing and depends on ER-phagy.

Nature cell biology·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
Same journal

Informative Relational Learning for Adverse Reaction Prediction with Enhanced Generalization to Novel Drugs.

Bioinformatics (Oxford, England)·2026
Same journal

An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.

Bioinformatics (Oxford, England)·2026
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
See all related articles

This study introduces a scalable semi-automatic method for 3D microscopy image segmentation using crowd labor. The Dual Point Decision Process efficiently segments 3D structures by distributing simple tasks to online workers.

Area of Science:

  • * Computational Biology
  • * Image Analysis
  • * Microscopy

Background:

  • * Accurate segmentation of 3D structures in microscopy is crucial for biological research.
  • * Existing methods often struggle with scalability and automation for large 3D datasets.

Purpose of the Study:

  • * To present a novel, scalable, semi-automatic approach for segmenting 3D structures in 3D microscopy images.
  • * To leverage distributed micro-labor for efficient image segmentation tasks.

Main Methods:

  • * Development of the Dual Point Decision Process (DPDP) for image segmentation.
  • * Distribution of segmentation tasks to a large pool of micro-labor workers via Amazon Mechanical Turk.
  • * Each worker answers simple binary questions about point placement within images.

More Related Videos

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Related Experiment Videos

Last Updated: May 12, 2026

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

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Main Results:

  • * The DPDP offers a scalable solution for 3D structure segmentation.
  • * Semi-automatic segmentation achieved through a distributed micro-labor approach.
  • * Demonstrates the feasibility of using crowd-sourcing for complex image analysis tasks.

Conclusions:

  • * The Dual Point Decision Process provides an effective and scalable method for 3D microscopy image segmentation.
  • * This approach utilizes crowd-sourcing to overcome limitations of traditional segmentation techniques.
  • * The described method offers a practical solution for analyzing large 3D microscopy datasets.