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

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...
Classification of Bones01:18

Classification of Bones

The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The long...
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...

You might also read

Related Articles

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

Sort by
Same author

Psychological resilience mediates the impact of anxiety and insomnia on health-related quality of life in maintenance dialysis patients.

Renal failure·2026
Same author

Programmable Anomalous Photovoltaics Enabled by Light-Electric Dual-Field Control.

Journal of the American Chemical Society·2026
Same author

Dementia risk by metabolic health and obesity in two prospective cohorts.

BMC medicine·2026
Same author

Association of volatile organic compounds with serum lactate dehydrogenase levels in the general adults.

International archives of occupational and environmental health·2026
Same author

Learning like a radiologist: a medical vision-language model for radiological image analysis via curriculum learning.

NPJ digital medicine·2026
Same author

High-Sensitivity Graphene/2D Perovskite Hybrid Photodetector for Visible-Light Sensing on Silicon Nitride Photonic Integrated Platform.

ACS nano·2026

Related Experiment Video

Updated: May 15, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Robust MR spine detection using hierarchical learning and local articulated model.

Yiqiang Zhan1, Dewan Maneesh, Martin Harder

  • 1Siemens Medical Solutions USA, Inc., Malvern, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|January 5, 2013
PubMed
Summary

This study introduces a robust auto-spine detection system for accurate vertebrae and disc localization. The novel hierarchical approach and articulated model enhance performance, especially in cases with severe spinal diseases and imaging artifacts.

More Related Videos

Imaging Dendritic Spines of Rat Primary Hippocampal Neurons using Structured Illumination Microscopy
14:11

Imaging Dendritic Spines of Rat Primary Hippocampal Neurons using Structured Illumination Microscopy

Published on: May 4, 2014

Related Experiment Videos

Last Updated: May 15, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Imaging Dendritic Spines of Rat Primary Hippocampal Neurons using Structured Illumination Microscopy
14:11

Imaging Dendritic Spines of Rat Primary Hippocampal Neurons using Structured Illumination Microscopy

Published on: May 4, 2014

Area of Science:

  • Medical Imaging
  • Radiology
  • Computer Vision

Background:

  • Accurate auto-spine detection (vertebrae and intervertebral disc localization/labeling) is crucial for clinical applications.
  • Existing methods struggle with robustness, particularly in the presence of severe spinal diseases (e.g., scoliosis) and imaging artifacts (e.g., metal artifacts in MRI).

Purpose of the Study:

  • To develop a clinically acceptable auto-spine detection system with high robustness to severe diseases and imaging artifacts.
  • To emulate a radiologist's approach using a hierarchical detection strategy and a local articulated model.

Main Methods:

  • A hierarchical strategy employing specialized detectors for anchor vertebrae, bundle vertebrae, and intervertebral discs.
  • Concurrent detection of anchor vertebrae for redundant appearance cues, robust to local artifacts.
  • Mutual determination of bundle vertebrae labels based on anchor vertebrae for enhanced robustness.
  • Disc localization derived from a response cloud from disc detectors, resilient to voxel-level errors.
  • A local articulated model to capture non-rigid spine anatomy and fuse appearance cues, accommodating abnormal geometries.

Main Results:

  • The proposed method demonstrates robust performance in auto-spine detection.
  • The system shows particular effectiveness in handling cases with severe spinal diseases and imaging artifacts.
  • Validation on 300 MR spine scout scans confirms the method's reliability.

Conclusions:

  • The developed auto-spine detection system achieves high robustness, addressing limitations of previous methods.
  • The hierarchical detection and local articulated modeling effectively handle complex spinal anatomies and imaging challenges.
  • This approach holds promise for improved clinical workflow and diagnostic accuracy in spine imaging.