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Related Concept Videos

Structural Classification of Joints01:20

Structural Classification of Joints

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...
Functional Classification of Joints01:09

Functional Classification of Joints

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 immobile...

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Related Experiment Video

Updated: Jun 8, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3D knowledge-based segmentation using pose-invariant higher-order graphs.

Chaohui Wang1, Olivier Teboul, Fabrice Michel

  • 1Laboratoire MAS, Ecole Centrale de Paris, France. chaohui.wang@ecp.fr

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|October 1, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel higher-order Markov Random Field (MRF) model for 3D medical image segmentation. The method effectively incorporates pose-invariant priors for improved segmentation of challenging datasets, such as human skeletal muscle tissues.

Related Experiment Videos

Last Updated: Jun 8, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

  • Medical Image Analysis
  • Computer Vision
  • Biomedical Engineering

Background:

  • Segmentation is crucial in medical imaging but often ill-posed.
  • Prior knowledge integration is key to addressing segmentation challenges.
  • Existing methods require pose estimation and balancing data-attraction with prior models.

Purpose of the Study:

  • To propose a novel higher-order Markov Random Field (MRF) model.
  • To encode pose-invariant priors for 3D medical image segmentation.
  • To handle challenging datasets and improve segmentation accuracy.

Main Methods:

  • Developed a higher-order Markov Random Field (MRF) model.
  • Encoded pose-invariant priors within the MRF framework.
  • Utilized machine learning for data support (singleton terms) and higher-order terms for prior constraints.
  • Employed a dual-decomposition-based inference method for optimal solution recovery.

Main Results:

  • Demonstrated promising results on challenging 3D segmentation tasks.
  • Successfully segmented tissue classes within the human skeletal muscle.
  • The proposed model effectively integrates pose-invariant priors.

Conclusions:

  • The novel higher-order MRF model shows significant potential for 3D medical image segmentation.
  • The approach addresses the ill-posed nature of segmentation by incorporating pose-invariant priors.
  • Effective for segmenting complex anatomical structures like human skeletal muscle.