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 Experiment Video

Updated: Dec 30, 2025

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

43.3K

Deep-learning-based fully automatic spine centerline detection in CT data.

Roman Jakubicek, Jiri Chmelik, Petr Ourednicek

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 18, 2020
    PubMed
    Summary
    This summary is machine-generated.

    Related Concept Videos

    You might also read

    Related Articles

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

    Sort by
    Same author

    BEAMSTER: Brain mEtAstases segMentation for STEreotactic Radiotherapy, A Retrospective MRI Dataset with Expert Segmentations.

    Scientific data·2026
    Same author

    Therapist-guided virtual reality exposure therapy for fear of heights: evidence from a multiple-baseline single-case experimental design.

    Scientific reports·2026
    Same author

    Segmentation of spinal rootlets across MRI contrasts with RootletSeg.

    Scientific reports·2026
    Same author

    Automated attenuation analysis of CT pulmonary angiography identifies peripheral hyperperfusion as a prognostic marker in non-surgical chronic thromboembolic pulmonary hypertension (CTEPH).

    Quantitative imaging in medicine and surgery·2026
    Same author

    Basecalling-free resistance gene identification using a hybrid transformer in raw nanopore signals.

    Frontiers in microbiology·2026
    Same author

    AMOchar: an amorphous MnOx functionalized biochar to stabilize metal(loid)s in soil and optimize phytostabilization.

    Scientific reports·2025

    This study introduces an automated method using two convolution neural networks (CNNs) and a spine tracing algorithm for fast and accurate spinal centerline detection in CT scans.

    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Computational Anatomy

    Background:

    • Accurate spinal centerline detection is crucial for medical image analysis.
    • Existing methods can be time-consuming and struggle with complex spinal anatomies.

    Purpose of the Study:

    • To develop a fully automatic and robust approach for spinal centerline detection.
    • To improve the speed and accuracy of spinal centerline extraction from CT scans.

    Main Methods:

    • Utilized two convolution neural networks (CNNs) for image analysis.
    • Integrated a spine tracing algorithm with a population optimization algorithm.
    • Evaluated the approach on 130 CT scans, including challenging cases.

    Main Results:

    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

    8.4K
    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

    3.5K

    Related Experiment Videos

    Last Updated: Dec 30, 2025

    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

    43.3K
    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

    8.4K
    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

    3.5K
    • Achieved almost 90% accuracy in determining spinal centerlines.
    • Demonstrated a computing time of fewer than 20 seconds per scan.
    • Showcased robustness in detecting centerlines even in heavily distorted cases.

    Conclusions:

    • The combined CNN and spine tracing approach offers a fast and reliable solution for automated spinal centerline detection.
    • This method has the potential to significantly enhance the efficiency of spinal analysis in clinical settings.