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

Vertebral Column: Regions and Curvature01:16

Vertebral Column: Regions and Curvature

4.2K
The vertebral column or spine is a flexible column that supports the head, neck, and body and  allows for their movements. It also protects the spinal cord.
Regions of the Vertebral Column
In an adult, the spine is subdivided into five regions: the cervical, the thoracic, the lumbar, the sacral, and the coccygeal region. The spine initially develops as a series of 33 vertebrae; after 20 years of age, the nine bones in the sacral region, five sacral, and four coccygeal bones fuse to form...
4.2K
General Structure of a Vertebra01:30

General Structure of a Vertebra

3.7K
A typical vertebra, with the exception of the sacrum and coccyx, consists of a body, a vertebral arch, and seven different projections termed processes. The anterior portion of the vertebrae, the body, supports about half the body’s weight. The vertebral bodies progressively increase in size and thickness from the cervical region to the lumbar region of the vertebral column. The intervertebral discs present between the bodies of adjacent vertebrae firmly unites them, forming a continuous...
3.7K
Spinal Cord: Cross-sectional Anatomy01:16

Spinal Cord: Cross-sectional Anatomy

2.5K
The cross-sectional anatomy of the spinal cord offers a detailed view of its complex structure and function within the central nervous system. At the core of the spinal cord lies the gray matter, characterized by its butterfly or "H"-shaped appearance in cross-section. This central region is enveloped by white matter, with the overall structure divided into symmetrical halves by the dorsal median sulcus and the ventral median fissure.
Gray Matter and its Components
Central to the gray matter is...
2.5K
Classification of Bones01:18

Classification of Bones

7.6K
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...
7.6K

You might also read

Related Articles

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

Sort by
Same author

MammoDenseSegNet: A Context-Aware Deep Learning Model for Dense Tissue Segmentation in Digital Mammograms.

Journal of imaging informatics in medicine·2026
Same author

The Impact of Training Dental Students to Use an Artificial Intelligence-Based Platform for Pulp Exposure Prediction Prior to Deep Caries Excavation: A Proof-of-Concept Randomised Controlled Trial.

International endodontic journal·2025
Same author

Improving the Real-Time Classification of Disease Severity in Ulcerative Colitis: Artificial Intelligence as the Trigger for a Second Opinion.

The American journal of gastroenterology·2025
Same author

Prediction of radiological decision errors from longitudinal analysis of gaze and image features.

Artificial intelligence in medicine·2024
Same author

CT-Derived Features as Predictors of Clot Burden and Resolution.

Bioengineering (Basel, Switzerland)·2024
Same author

Centerline-guided reinforcement learning model for pancreatic duct identifications.

Journal of medical imaging (Bellingham, Wash.)·2024

Related Experiment Video

Updated: Sep 22, 2025

Three and Four-Dimensional Visualization and Analysis Approaches to Study Vertebrate Axial Elongation and Segmentation
12:59

Three and Four-Dimensional Visualization and Analysis Approaches to Study Vertebrate Axial Elongation and Segmentation

Published on: February 28, 2021

3.8K

A deep learning framework for vertebral morphometry and Cobb angle measurement with external validation.

Danis Alukaev1, Semen Kiselev1, Tamerlan Mustafaev1,2

  • 1AI Lab, Innopolis University, Universitetskaya St 1, 420500, Innopolis, Republic of Tatarstan, Russian Federation.

European Spine Journal : Official Publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
|May 21, 2022
PubMed
Summary

This study introduces an automated deep learning framework for precise spinal measurements from CT scans. The framework accurately measures vertebral morphometry and Cobb angles, demonstrating excellent performance on external data.

Keywords:
Artificial intelligenceCobb angleComputed tomographyDeep learningSpineVertebral morphometry

More Related Videos

Precision Measurements and Parametric Models of Vertebral Endplates
10:35

Precision Measurements and Parametric Models of Vertebral Endplates

Published on: September 17, 2019

6.6K
Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans
10:23

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Published on: September 8, 2023

3.1K

Related Experiment Videos

Last Updated: Sep 22, 2025

Three and Four-Dimensional Visualization and Analysis Approaches to Study Vertebrate Axial Elongation and Segmentation
12:59

Three and Four-Dimensional Visualization and Analysis Approaches to Study Vertebrate Axial Elongation and Segmentation

Published on: February 28, 2021

3.8K
Precision Measurements and Parametric Models of Vertebral Endplates
10:35

Precision Measurements and Parametric Models of Vertebral Endplates

Published on: September 17, 2019

6.6K
Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans
10:23

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Published on: September 8, 2023

3.1K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Spine Surgery

Background:

  • Accurate vertebral morphometry and spinal curvature assessment are crucial for diagnosing and managing spinal conditions.
  • Manual measurements are time-consuming and prone to inter-observer variability.
  • Deep learning (DL) offers potential for automating these complex measurements.

Purpose of the Study:

  • To develop and validate a fully automated deep learning framework for vertebral morphometry and Cobb angle measurement from 3D CT images.
  • To assess the framework's performance on an external, independent dataset.

Main Methods:

  • A DL architecture using an ensemble of U-Nets was employed for vertebrae localization and segmentation.
  • Automated measurements of vertebral body (VB) and intervertebral disk (IVD) heights were performed.
  • Coronal and sagittal Cobb angles (thoracic kyphosis, lumbar lordosis) were calculated using machine learning techniques.
  • The framework was trained on 1725 vertebrae from 160 CT scans and validated on 157 vertebrae from 15 CT scans.

Main Results:

  • Mean absolute errors were 1.17 ± 0.40 mm for VB heights, 0.54 ± 0.21 mm for IVD heights, and 3.42 ± 1.36° for Cobb angles.
  • Excellent agreement was observed with manual measurements, with Pearson's correlation coefficients of 0.943 (VB height), 0.928 (IVD height), and 0.996 (Cobb angles).
  • Maximal absolute errors were 2.51 mm (VB height), 1.64 mm (IVD height), and 5.52° (Cobb angles).

Conclusions:

  • The proposed DL framework achieves high accuracy in automated vertebral morphometry and Cobb angle measurement.
  • Results are comparable to existing DL methods and demonstrate excellent external validation, confirming scalability.
  • The framework offers an efficient and reliable solution for spinal analysis in clinical settings.