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

Machine Learning Models Used to Predict Abdominal Aortic Aneurysm Growth and Rupture: A Systematic Review and Critical Appraisal.

Annals of vascular surgery·2026
Same author

EZH2 Inhibition Reshapes 3D Chromatin Architecture to Induce Immunogenic Phenotype in Small Cell Lung Cancer.

bioRxiv : the preprint server for biology·2026
Same author

Topology-Based Biomarkers Accurately Predict Breast Cancer Outcome and Survival.

Cancer research·2026
Same author

MRI-based patient selection for active surveillance in prostate cancer using U-Found: a generalized deep learning model.

Cancer imaging : the official publication of the International Cancer Imaging Society·2026
Same author

Prognostic significance of CD8+ T cell Spatial Biomarkers in ER+ and ER- breast cancer: A retrospective cohort study.

PLoS medicine·2025
Same author

Measuring and predicting where and when pathologists focus their visual attention while grading whole slide images of cancer.

Medical image analysis·2025

Related Experiment Video

Updated: Jul 11, 2026

In-vivo Detection of Protein-protein Interactions on Micro-patterned Surfaces
07:42

In-vivo Detection of Protein-protein Interactions on Micro-patterned Surfaces

Published on: March 19, 2010

Detection and visualization of surface-pockets to enable phenotyping studies.

Kishore Mosaliganti1, Firdaus Janoos, Richard Sharp

  • 1Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA. mosaligk@cse.ohio-state.edu

IEEE Transactions on Medical Imaging
|September 28, 2007
PubMed
Summary

This study introduces a novel technique for detecting surface pockets using propagating fronts and feature space analysis. The method accurately identifies pockets in genetically modified mouse placenta for phenotyping, validated against ground-truth data.

More Related Videos

Mechano-Node-Pore Sensing: A Rapid, Label-Free Platform for Multi-Parameter Single-Cell Viscoelastic Measurements
05:49

Mechano-Node-Pore Sensing: A Rapid, Label-Free Platform for Multi-Parameter Single-Cell Viscoelastic Measurements

Published on: December 2, 2022

Multimodal Analytical Platform on a Multiplexed Surface Plasmon Resonance Imaging Chip for the Analysis of Extracellular Vesicle Subsets
06:12

Multimodal Analytical Platform on a Multiplexed Surface Plasmon Resonance Imaging Chip for the Analysis of Extracellular Vesicle Subsets

Published on: March 17, 2023

Related Experiment Videos

Last Updated: Jul 11, 2026

In-vivo Detection of Protein-protein Interactions on Micro-patterned Surfaces
07:42

In-vivo Detection of Protein-protein Interactions on Micro-patterned Surfaces

Published on: March 19, 2010

Mechano-Node-Pore Sensing: A Rapid, Label-Free Platform for Multi-Parameter Single-Cell Viscoelastic Measurements
05:49

Mechano-Node-Pore Sensing: A Rapid, Label-Free Platform for Multi-Parameter Single-Cell Viscoelastic Measurements

Published on: December 2, 2022

Multimodal Analytical Platform on a Multiplexed Surface Plasmon Resonance Imaging Chip for the Analysis of Extracellular Vesicle Subsets
06:12

Multimodal Analytical Platform on a Multiplexed Surface Plasmon Resonance Imaging Chip for the Analysis of Extracellular Vesicle Subsets

Published on: March 17, 2023

Area of Science:

  • Computational geometry
  • Image analysis
  • Biomedical imaging

Background:

  • Accurate detection of surface features like pockets is crucial for biological studies.
  • Traditional methods may lack precision in characterizing complex surface topographies.

Purpose of the Study:

  • To propose and validate a new computational technique for detecting and analyzing surface pockets.
  • To apply this technique to identify phenotypical differences in genetically modified mouse placentas.

Main Methods:

  • Utilizing a sequence of propagating fronts converging to a surface of interest.
  • Computing a correspondence function to define local feature size via evolution distance.
  • Extracting surface pockets as clusters in a defined feature space.
  • Employing level-set initialization for scale-space determination.

Main Results:

  • Successfully detected and characterized surface pockets using the proposed method.
  • Demonstrated the technique's efficacy in a case study involving genetically modified mouse placenta phenotyping.
  • Achieved validation against manually verified ground-truth data, confirming accuracy.

Conclusions:

  • The proposed technique offers a robust and accurate approach for surface pocket detection.
  • This method facilitates detailed phenotypical analysis, particularly in biological and medical research.
  • The validated results highlight the potential of this technique for scientific discovery.