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

Knee Joint01:23

Knee Joint

2.6K
The knee joint is the most complicated joint in the body. It consists of three articulations– two tibiofemoral and one patellofemoral. As is characteristic of synovial joints, the knee joint has a thin articular capsule that partially surrounds this joint cavity. Additionally, several ligaments, muscles, and cartilaginous structures support the movement of the knee.
A total of seven ligaments support the knee joint. The patellar ligament, which is also attached to the quadriceps femoris...
2.6K
Bones of the Lower Limb: Femur and Patella01:16

Bones of the Lower Limb: Femur and Patella

4.1K
The femur is the body's longest and strongest bone spanning the thigh region. Its head articulates with the acetabulum of the hip bone to form the hip joint. A minor indentation on the medial side of the femoral head, called the fovea capitis, serves as the site of attachment for the ligament of the head of the femur. This weak ligament spans the femur and acetabulum and supports the hip joint. The narrowed region below the head is the neck of the femur. The inclination angle between the...
4.1K
Structural Classification of Joints01:20

Structural Classification of Joints

5.4K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
5.4K
Bone Remodeling01:40

Bone Remodeling

38.9K
Bone remodeling is a continuous and balanced process of bone resorption by osteoclasts and bone formation by osteoblasts. In adults, it helps maintain bone mass and calcium homeostasis. While mechanical stress can stimulate turnover as part of the normal maintenance and reparative process, several hormones also regulate bone remodeling.
38.9K
Development of the Limb Synovial Joints01:07

Development of the Limb Synovial Joints

1.8K
Joints form during embryonic development in conjunction with the formation and growth of the associated bones. The embryonic tissue that gives rise to all bones, cartilage, and connective tissues of the body is called mesenchyme.
The mesenchymal stem cells differentiate into chondrocytes that form the hyaline cartilage, and later the cartilaginous model of the bone. This model further transforms into a bone. This process is known as endochondral ossification.
During development, the limbs...
1.8K
Functional Classification of Joints01:09

Functional Classification of Joints

5.6K
Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
5.6K

You might also read

Related Articles

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

Sort by
Same author

Misfortune Begets Fortune? Tailoring Facets in the MXene Oxidation Process for a Sensitive and Recyclable SERS Platform.

Nano letters·2026
Same author

High-Performance Multi-Walled Carbon Nanotubes-Organic Passivated Si Solar Cells Enabled by Spatially Selective Harvesting of High-Quality Sponges.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Ligilactobacillus salivarius Lbs57 Exerted Antibacterial Activity and Modulated Cecal Microbiota to Reduce Foodborne Pathogen Campylobacter jejuni.

Probiotics and antimicrobial proteins·2026
Same author

Asphaltene-Induced Deactivation and Solvent Regeneration of Polymer-Grafted Magnetic Nanodemulsifiers.

Langmuir : the ACS journal of surfaces and colloids·2026
Same author

All-Weather Adaptable Non-Woven Janus Graphene Fibers with Asymmetric Wettability, Reliable Flame Retardancy and Synergistic Evaporative Cooling-Ultrafast Joule Heating.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Solvent polarity and chalcogen electronegativity synergistically regulate excited-state proton transfer in BBYPD derivatives.

Photochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for Photobiology·2026

Related Experiment Video

Updated: Oct 26, 2025

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
09:32

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

9.9K

Multimodality-based knee joint modelling method with bone and cartilage structures for total knee arthroplasty.

Jiahe Chen1, Fuzhen Yuan2, Yu Shen1

  • 1School of Mechanical Engineering and Automation, Beihang University, Beijing, China.

The International Journal of Medical Robotics + Computer Assisted Surgery : MRCAS
|July 27, 2021
PubMed
Summary

This study presents a new method for creating accurate 3D knee joint models, including bone and cartilage, using fused MRI and CT scans. This approach enhances surgical guidance by improving the precision of anatomical models for knee surgery.

Keywords:
bone extractioncartilage extractionknee joint modellingmultimodal registrationmultimodal segmentation

More Related Videos

Tissue Collection and RNA Extraction from the Human Osteoarthritic Knee Joint
06:06

Tissue Collection and RNA Extraction from the Human Osteoarthritic Knee Joint

Published on: July 22, 2021

6.3K
The Use of Mixed Reality in Custom-Made Revision Hip Arthroplasty: A First Case Report
07:45

The Use of Mixed Reality in Custom-Made Revision Hip Arthroplasty: A First Case Report

Published on: August 4, 2022

3.6K

Related Experiment Videos

Last Updated: Oct 26, 2025

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
09:32

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

9.9K
Tissue Collection and RNA Extraction from the Human Osteoarthritic Knee Joint
06:06

Tissue Collection and RNA Extraction from the Human Osteoarthritic Knee Joint

Published on: July 22, 2021

6.3K
The Use of Mixed Reality in Custom-Made Revision Hip Arthroplasty: A First Case Report
07:45

The Use of Mixed Reality in Custom-Made Revision Hip Arthroplasty: A First Case Report

Published on: August 4, 2022

3.6K

Area of Science:

  • Medical imaging
  • Computational anatomy
  • Orthopedic surgery

Background:

  • Accurate knee joint modeling is crucial for effective surgical guidance.
  • Existing methods may struggle with spatial inconsistencies due to knee movement in scans.

Purpose of the Study:

  • To develop a robust and accurate knee joint modeling method.
  • To enable precise surgical guidance for knee surgery by incorporating bone and cartilage structures.

Main Methods:

  • A multimodality registration strategy fuses magnetic resonance (MR) and computed tomography (CT) images of the femur and tibia.
  • Automatic segmentation of femur, tibia, and cartilages is achieved through region of interest clustering and intensity analysis.
  • This addresses spatial inconsistencies caused by knee bending in CT/MR scans.

Main Results:

  • The registration error was found to be 1.13 ± 0.30 mm.
  • High Dice similarity coefficients were achieved for segmentation: femur (0.969), tibia (0.966), femoral cartilage (0.910), and tibial cartilage (0.872).

Conclusions:

  • The study validates the effectiveness of multimodality registration and segmentation for knee joint modeling.
  • The proposed method generates accurate 3D anatomical models of the knee with minimal user intervention.
  • This facilitates improved pre-operative planning and execution in knee surgery.