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

Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
At the receiving end, the boundary condition states that the voltage equals the product of the receiving-end impedance and current. This relationship is expressed as a function of the incident and...
Boundary Conditions for Current Density01:25

Boundary Conditions for Current Density

Current density becomes discontinuous across an interface of materials with different electrical conductivities. The normal component of the current density is continuous across the boundary.
Electrostatic Boundary Conditions01:16

Electrostatic Boundary Conditions

Consider an external electric field propagating through a homogeneous medium. When the electric field crosses the surface boundary of the medium, it undergoes a discontinuity. The electric field can be resolved into normal and tangential components. The amount by which the field changes at any boundary is given by the difference between the field components above and below the surface boundary.
The surface integral of an electric field is given by Gauss's law in integral form and is related to...

You might also read

Related Articles

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

Sort by
Same author

Topological modeling of gene expression in the brain with Huntington's disease reveals selective disruption of co-expression network.

Scientific reports·2026
Same author

Machine learning model for predicting the conversion to dementia using the Cube Copying Test.

Journal of Alzheimer's disease : JAD·2025
Same author

Autotaxin concentrations in peritoneal dialysis effluent reflect peritoneal function.

Therapeutic apheresis and dialysis : official peer-reviewed journal of the International Society for Apheresis, the Japanese Society for Apheresis, the Japanese Society for Dialysis Therapy·2024
Same author

Learning Self-Prior for Mesh Inpainting Using Self-Supervised Graph Convolutional Networks.

IEEE transactions on visualization and computer graphics·2024
Same author

Cavity and entrance pore development in ant plant hypocotyls.

Frontiers in plant science·2023
Same author

A Case of Usefulness of Auto-Injectable Adrenaline as a Prophylactic Countermeasure Against Bee Sting for Forestry Workers.

Workplace health & safety·2023
Same journal

Epidemiological characteristics of amebiasis in Japan from 2001 to 2022.

PloS one·2026
Same journal

Longitudinal associations of academic stress with eating related patterns, nutrition, somatic indicators, and depressive symptoms in university students: A study protocol.

PloS one·2026
Same journal

Pollution removal efficiency enhancement by agricultural biomass additions in constructed wetlands: A framework integrating meta-analysis with explainable machine learning.

PloS one·2026
Same journal

Insulation failure mapping on power transformer bushing using FRA and electrostatic simulation.

PloS one·2026
Same journal

Enhancing medical Q&A systems with multimodal knowledge graphs and dual-layer attention mechanisms.

PloS one·2026
Same journal

UAMP: Consistent video object segmentation with uncertainty-aware memory propagation.

PloS one·2026
See all related articles

Related Experiment Video

Updated: May 24, 2026

Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin
09:36

Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin

Published on: March 14, 2018

CT image segmentation using FEM with optimized boundary condition.

Hiroyuki Hishida1, Hiromasa Suzuki, Takashi Michikawa

  • 1Department of Precision Engineering, School of Engineering, The University of Tokyo, Bunkyo-ku, Tokyo, Japan. hishida@den.rcast.u-tokyo.ac.jp

Plos One
|March 6, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel CT image segmentation method using structural analysis and destruction analogy. This automated approach significantly reduces the time needed for segmenting mutant mouse skeletons, aiding genetic research.

More Related Videos

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images
09:21

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

Published on: February 18, 2015

Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo
12:54

Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo

Published on: October 2, 2021

Related Experiment Videos

Last Updated: May 24, 2026

Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin
09:36

Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin

Published on: March 14, 2018

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images
09:21

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

Published on: February 18, 2015

Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo
12:54

Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo

Published on: October 2, 2021

Area of Science:

  • Biomedical Imaging
  • Computational Biology
  • Medical Image Analysis

Background:

  • Manual segmentation of CT images, particularly for mutant mouse skeletons, is time-consuming and hinders genetic research.
  • Existing segmentation techniques lack a general method for complex biological structures like skeletons.
  • Automating CT image segmentation is crucial for efficient analysis of genetic variations.

Purpose of the Study:

  • To develop an automated CT image segmentation method for objects with dynamic structural characteristics.
  • To address the limitations of manual segmentation in analyzing mutant mouse models for genetic studies.
  • To introduce a novel approach for skeleton segmentation using structural analysis.

Main Methods:

  • The proposed method utilizes structural analysis via the finite element method (FEM) based on the concept of destruction analogy.
  • Finite elements are generated directly from CT image pixels, with candidate segmentation areas identified.
  • Destruction analogy is applied by iteratively removing high-strain pixels until the object is segmented.

Main Results:

  • The method successfully segments various types of CT imagery, demonstrating its versatility.
  • Structural analysis via FEM and destruction analogy provides a novel approach to image segmentation.
  • Automated segmentation significantly reduces the labor involved in analyzing large datasets of mutant mouse skeletons.

Conclusions:

  • The proposed destruction analogy-based structural analysis offers a novel and effective solution for CT image segmentation.
  • This automated method has the potential to accelerate genetic research by streamlining the analysis of skeletal structures.
  • The technique is applicable to various CT imaging scenarios, particularly for biological specimens.