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

Confocal Fluorescence Microscopy01:16

Confocal Fluorescence Microscopy

21.3K
Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...
21.3K
Imaging Studies VII: Vascular Imaging01:19

Imaging Studies VII: Vascular Imaging

394
DefinitionRenal angiography, also known as renal arteriography, is an imaging technique used to obtain a comprehensive view of blood flow and the vascular structure of blood vessels in the kidneys and surrounding areas.PurposeRenal angiography detects blood vessel abnormalities in the kidneys, such as aneurysms, stenosis, thrombosis, vascular tumors, and renal artery stenosis. It evaluates kidney function and guides interventional treatments like angioplasty or stent placement.Pre-Procedure...
394

You might also read

Related Articles

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

Sort by
Same author

SpaDiffHis: Sparse-point Guided Diffusion for Histopathology Image Synthesis with Contrastive Learning.

IEEE journal of biomedical and health informatics·2026
Same author

The ancient trading hubs of modern science: Bridging the divide between microscopists and data scientists.

Journal of microscopy·2026
Same author

Cross-Architecture Knowledge Distillation for Histopathological Image Analysis.

IEEE access : practical innovations, open solutions·2026
Same author

End-to-End Multimodal Multiple Instance Learning for Cancer Histopathology Classification with Dual-Attention Fusion.

Journal of medical systems·2026
Same author

Pathology Public Datasets for Artificial Intelligence: A Systematic Review.

Journal of imaging informatics in medicine·2026
Same author

An open-source deep learning-based toolbox for automated auditory brainstem response analyses (ABRA).

Scientific reports·2026
Same journal

Axon and Myelin Sheath Segmentation in Electron Microscopy Images using Meta Learning.

IEEE Applied Imagery Pattern Recognition Workshop : [proceedings]. IEEE Applied Imagery Pattern Recognition Workshop·2023
Same journal

Deep Learning-Based Cell Detection and Extraction in Thin Blood Smears for Malaria Diagnosis.

IEEE Applied Imagery Pattern Recognition Workshop : [proceedings]. IEEE Applied Imagery Pattern Recognition Workshop·2022
Same journal

Extending U-Net Network for Improved Nuclei Instance Segmentation Accuracy in Histopathology Images.

IEEE Applied Imagery Pattern Recognition Workshop : [proceedings]. IEEE Applied Imagery Pattern Recognition Workshop·2022
Same journal

Ensemble of Deep Learning Cascades for Segmentation of Blood Vessels in Confocal Microscopy Images.

IEEE Applied Imagery Pattern Recognition Workshop : [proceedings]. IEEE Applied Imagery Pattern Recognition Workshop·2022
Same journal

Patch-Based Semantic Segmentation for Detecting Arterioles and Venules in Epifluorescence Imagery.

IEEE Applied Imagery Pattern Recognition Workshop : [proceedings]. IEEE Applied Imagery Pattern Recognition Workshop·2020
Same journal

The National Library of Medicine Pill Image Recognition Challenge: An Initial Report.

IEEE Applied Imagery Pattern Recognition Workshop : [proceedings]. IEEE Applied Imagery Pattern Recognition Workshop·2018
See all related articles

Related Experiment Video

Updated: Feb 18, 2026

Quantification of Vascular Parameters in Whole Mount Retinas of Mice with Non-Proliferative and Proliferative Retinopathies
12:28

Quantification of Vascular Parameters in Whole Mount Retinas of Mice with Non-Proliferative and Proliferative Retinopathies

Published on: March 12, 2022

4.3K

Confocal Vessel Structure Segmentation with Optimized Feature Bank and Random Forests.

Yasmin M Kassim1, V B Surya Prasath1, Olga V Glinskii2,3

  • 1Computational Imaging and VisAnalysis (CIVA) Lab Department of Computer Science, University of Missouri-Columbia, Columbia, MO 65211 USA.

IEEE Applied Imagery Pattern Recognition Workshop : [Proceedings]. IEEE Applied Imagery Pattern Recognition Workshop
|November 21, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces optimized features and random forest classification for confocal microscopy vessel segmentation, improving accuracy in challenging conditions.

More Related Videos

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment
10:39

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

Published on: May 24, 2022

2.8K
Image-guided, Laser-based Fabrication of Vascular-derived Microfluidic Networks
10:53

Image-guided, Laser-based Fabrication of Vascular-derived Microfluidic Networks

Published on: January 3, 2017

10.4K

Related Experiment Videos

Last Updated: Feb 18, 2026

Quantification of Vascular Parameters in Whole Mount Retinas of Mice with Non-Proliferative and Proliferative Retinopathies
12:28

Quantification of Vascular Parameters in Whole Mount Retinas of Mice with Non-Proliferative and Proliferative Retinopathies

Published on: March 12, 2022

4.3K
A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment
10:39

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

Published on: May 24, 2022

2.8K
Image-guided, Laser-based Fabrication of Vascular-derived Microfluidic Networks
10:53

Image-guided, Laser-based Fabrication of Vascular-derived Microfluidic Networks

Published on: January 3, 2017

10.4K

Area of Science:

  • Biomedical imaging
  • Computational biology

Background:

  • Confocal microscopy is crucial for visualizing biological structures.
  • Accurate vessel segmentation is essential for analyzing vascular networks.
  • Existing segmentation methods struggle with complex vascular patterns.

Purpose of the Study:

  • To develop an improved vessel segmentation method for confocal microscopy images.
  • To enhance the accuracy of segmentation using optimized, vessel-specific features.
  • To evaluate the performance against traditional global segmentation techniques.

Main Methods:

  • Utilized multi-scale, vessel-specific features including Hessian eigenvalues, Laplacian of Gaussians (LoG), and oriented second derivatives.
  • Employed a random forest classifier trained on expert-annotated ground-truth data.
  • Applied intensity masking with a LoG scale map to refine feature detection.

Main Results:

  • Achieved superior binary segmentation results in challenging confocal microscopy images.
  • Demonstrated enhanced capability in capturing curvilinear vascular structures.
  • Outperformed global segmentation approaches in experimental evaluations on mice dura mater data.

Conclusions:

  • The proposed method significantly improves vessel segmentation accuracy in confocal microscopy.
  • Optimized features and random forest classification offer a robust solution for complex vascular imaging.
  • This approach provides a valuable tool for quantitative analysis of vascular networks.