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

Structural Classification of Joints01:20

Structural Classification of Joints

6.0K
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...
6.0K
Functional Classification of Joints01:09

Functional Classification of Joints

5.9K
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...
5.9K
Knee Joint01:23

Knee Joint

2.8K
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...
2.8K
Bones of the Lower Limb: Femur and Patella01:16

Bones of the Lower Limb: Femur and Patella

4.3K
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...
4.3K
Deformation of Member under Multiple Loadings01:11

Deformation of Member under Multiple Loadings

300
When a rod is made of different materials or has various cross-sections, it must be divided into parts that meet the necessary conditions for determining the deformation. These parts are each characterized by their internal force, cross-sectional area, length, and modulus of elasticity. These parameters are then used to compute the deformation of the entire rod.
In the case of a member with a variable cross-section, the strain is not constant but depends on the position. The deformation of an...
300
Development of the Limb Synovial Joints01:07

Development of the Limb Synovial Joints

1.9K
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...
1.9K

You might also read

Related Articles

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

Sort by
Same author

mpMRI-Based Risk Estimation to Optimize Prostate Cancer Patient Selection for Active Surveillance.

Cancers·2026
Same author

Effective waste management towards promoting circular economy.

Environmental science and pollution research international·2026
Same author

Efficient and Scalable Point Cloud Generation With Sparse Point-Voxel Diffusion Models.

IEEE transactions on neural networks and learning systems·2025
Same author

Sustainable waste management and valorization within the circular economy era.

Environmental science and pollution research international·2025
Same author

Integration of Habitat Risk Score in Radiation Therapy for Prostate Cancer.

International journal of radiation oncology, biology, physics·2025
Same author

Interactive digital twins enabling responsible extended reality applications.

Scientific reports·2025

Related Experiment Video

Updated: Nov 11, 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

10.0K

Regularized multi-structural shape modeling of the knee complex based on deep functional maps.

Konstantinos Filip1, Evangelia I Zacharaki1, Konstantinos Moustakas1

  • 1Department of Electrical and Computer Engineering, University of Patras, Greece.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|March 23, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces advanced Statistical Shape Models (SSMs) using Functional Maps and deep learning to improve 3D anatomical modeling. The approach enhances shape reconstruction and estimation for complex structures like the human knee.

Keywords:
Deep functional mapsKnee modelingMissing structure estimationMulti-structure statistical shape modelShape correspondenceShape reconstruction

More Related Videos

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

8.2K
Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

7.4K

Related Experiment Videos

Last Updated: Nov 11, 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

10.0K
Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

8.2K
Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

7.4K

Area of Science:

  • Medical Image Analysis
  • Computer Vision
  • Computational Anatomy

Background:

  • Statistical Shape Models (SSMs) effectively guide image segmentation by incorporating prior knowledge of anatomical shape and variation.
  • Accurate statistical shape analysis requires robust methods for establishing point correspondences on anatomical surfaces.

Purpose of the Study:

  • To develop novel multiple-structure Statistical Shape Models (SSMs) for improved anatomical shape analysis and 3D model reconstruction.
  • To address the challenge of point correspondence in statistical shape analysis using the Functional Maps framework.
  • To enhance SSM capabilities by integrating deep learning techniques for better shape variation capture.

Main Methods:

  • Construction of multiple-structure SSMs using Canonical Correlation Analysis to model relationships between adjacent anatomical structures.
  • Application of the Functional Maps framework to solve the surface correspondence problem, generalizing point-to-point mapping.
  • Integration of deep learning principles within the Functional Maps framework to improve shape variation modeling.
  • Utilizing regularization terms on shape likelihood for enhanced structure representation during shape inference.

Main Results:

  • Demonstrated reliable structure representations through shape inference with regularization.
  • Successfully addressed the point correspondence problem using the generalized Functional Maps framework.
  • Enhanced the ability of SSMs to capture complex shape variations by incorporating deep learning.
  • Validated the approach through the creation of 3D models of the human knee complex.

Conclusions:

  • The proposed method effectively improves 3D anatomical modeling, particularly for complex structures like the human knee.
  • The integration of Functional Maps and deep learning offers a powerful framework for statistical shape analysis and reconstruction.
  • The approach shows significant potential in applications involving incomplete or noisy shape data and missing structure estimation.