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

Imaging Studies VII: Vascular Imaging

Imaging Studies VII: Vascular Imaging

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...

You might also read

Related Articles

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

Sort by

Same author

Modeling development of tertiary lymphoid structures in pulmonary tuberculosis by 3D profiling and trajectory analysis.

bioRxiv : the preprint server for biology·2026

Same author

A Framework for Comparing Mouse Neoantigen Immunogenicity.

bioRxiv : the preprint server for biology·2026

Same author

Clocks and Dominoes: Timing Mechanisms of Embryogenesis.

bioRxiv : the preprint server for biology·2026

Same author

Task-Specific Directions: Definition, Exploration, and Utilization in Parameter Efficient Fine-Tuning.

IEEE transactions on pattern analysis and machine intelligence·2026

Same author

SmartEM: machine learning-guided electron microscopy.

Nature methods·2025

Same author

SynAnno: Interactive Guided Proofreading of Synaptic Annotations.

IEEE transactions on visualization and computer graphics·2025

Same journal

MesoSplats: Texture Synthesis with Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026

Same journal

GLLA: A Unified Force-Directed Graph Layout Framework Supporting Local Adjustments.

IEEE transactions on visualization and computer graphics·2026

Same journal

Multi-Perception Crowd: Learning to combine entity and implicit perception for diverse crowd simulation.

IEEE transactions on visualization and computer graphics·2026

Same journal

Hiding in Plain Sight: Camouflaging Real-world Objects.

IEEE transactions on visualization and computer graphics·2026

Same journal

RTF2Mesh: Restricted Tangent Face Based Mesh Compression With Neural Displacement Fields.

IEEE transactions on visualization and computer graphics·2026

Same journal

Practical Occluder Generation for Mobile Games.

IEEE transactions on visualization and computer graphics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 27, 2025

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions

Published on: September 22, 2013

Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data.

Simon Warchol, Robert Krueger, Ajit Johnson Nirmal

IEEE Transactions on Visualization and Computer Graphics

|September 28, 2022

Summary

This summary is machine-generated.

Visinity is a new visual analytics system for analyzing cell interactions in whole-slide tissue images. It helps researchers understand cancer microenvironments and immune responses by examining spatial patterns and cell neighborhoods.

More Related Videos

Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

Published on: April 7, 2023

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Related Experiment Videos

Last Updated: Aug 27, 2025

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions

Published on: September 22, 2013

Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

Published on: April 7, 2023

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Area of Science:

Computational Biology
Bioinformatics
Digital Pathology

Background:

Highly-multiplexed imaging generates unprecedented tissue detail.
Current analysis methods focus on per-cell data, neglecting spatial context.
Understanding cell-cell interactions in the tumor microenvironment is crucial for cancer research.

Purpose of the Study:

To introduce Visinity, a scalable visual analytics system for analyzing cell interaction patterns in whole-slide multiplexed tissue images.
To enable the study of cell neighborhoods and their role in biological processes.
To facilitate exploratory and confirmatory analysis of spatial patterns in cancer tissues.

Main Methods:

Visinity employs fast regional neighborhood computation and unsupervised learning.
It quantifies, compares, and groups cells based on their surrounding cellular neighborhoods.
The system features a scalable image viewer and coordinated views for visual analysis.

Main Results:

Visinity enables interactive exploration of spatial patterns across tissue cohorts.
Users can query for specific patterns to determine statistical significance.
Case studies demonstrated identification of biological processes and novel immune-tumor interactions.

Conclusions:

Visinity offers a novel approach to analyze cell interaction patterns in multiplexed tissue imaging.
The system aids in understanding the complex cancer microenvironment and immune responses.
Visinity facilitates discovery of biological insights and validation of hypotheses in cancer research.