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

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

You might also read

Related Articles

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

Sort by
Same author

Towards the construction of a virtual yeast.

Nature·2026
Same author

IQE-CLIP: Instance-Aware Query Embedding for Zero-/Few-Shot Anomaly Detection in Medical Domain.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

CLIS: Causality-inspired Longitudinal Image Synthesis and its application to Alzheimer's disease characterization.

Medical image analysis·2026
Same author

Leveraging Text-Modulated Semantic Guidance for Low-Light Endoscopic Image Enhancement.

IEEE transactions on medical imaging·2026
Same author

Towards generalizable AI in medicine via Generalist-Specialist Collaboration.

Nature biomedical engineering·2026
Same author

FedSemiDG: Domain generalized federated semi-supervised medical image segmentation.

Medical image analysis·2026

Related Experiment Video

Updated: May 15, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Spine detection in CT and MR using iterated marginal space learning.

B Michael Kelm1, Michael Wels, S Kevin Zhou

  • 1Imaging and Computer Vision, Siemens Corporate Technology, Erlangen, Germany.

Medical Image Analysis
|December 26, 2012
PubMed
Summary

This study introduces a novel method for automatically detecting and labeling spinal disks in MRI and CT scans. The approach achieves high accuracy and speed, crucial for computer-aided diagnosis applications.

Keywords:
Generative-discriminative detectionMarginal space learningSpinal disk segmentationSpine detectionVertebra segmentation

More Related Videos

Optimizing Minimally Invasive Spine Surgery: A Fully 3D CT O-Arm Navigated Workflow in MIS TLIF
08:34

Optimizing Minimally Invasive Spine Surgery: A Fully 3D CT O-Arm Navigated Workflow in MIS TLIF

Published on: October 17, 2025

Related Experiment Videos

Last Updated: May 15, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Optimizing Minimally Invasive Spine Surgery: A Fully 3D CT O-Arm Navigated Workflow in MIS TLIF
08:34

Optimizing Minimally Invasive Spine Surgery: A Fully 3D CT O-Arm Navigated Workflow in MIS TLIF

Published on: October 17, 2025

Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Spinal Imaging

Background:

  • Accurate 3D positioning and labeling of spinal disks and vertebrae are essential for Magnetic Resonance (MR) and Computed Tomography (CT) examinations.
  • Automated methods are needed for scan alignment, segmentation, and analysis in Computer Aided Diagnosis (CAD).

Purpose of the Study:

  • To present a novel, fully automatic, and robust method for simultaneous detection and labeling of spinal disks.
  • To enable precise 3D positioning, angulation, and labeling of spinal components for improved diagnostic accuracy.

Main Methods:

  • Combines Marginal Space Learning (MSL) for efficient object detection with a generative anatomical network incorporating relative pose information.
  • Utilizes an iterative MSL for high-sensitivity candidate generation and an anatomical network for selecting the most likely detections.
  • Proposes an optional case-adaptive segmentation approach for spinal disks and vertebrae in both MR and CT data.

Main Results:

  • The system achieves high accuracy and speed on both MR and CT datasets.
  • On MR data, spinal disks are detected with 98.6% sensitivity and 0.073 false positives per volume in 11.5s.
  • On CT data, comparable sensitivity (98.0%) and localization accuracy (2.4mm/3.2mm position error, 3.9°/4.5° angular error in MR/CT) were achieved.

Conclusions:

  • The proposed learning-based approach is effective for both MR and CT spinal imaging.
  • The method offers superior accuracy and speed compared to existing systems, facilitating automated analysis in CAD applications.
  • The system demonstrates excellent performance in detecting, labeling, and localizing spinal disks, paving the way for advanced diagnostic tools.