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: Nov 15, 2025

Augmented Reality Navigation-Guided Core Decompression for Osteonecrosis of Femoral Head
06:17

Augmented Reality Navigation-Guided Core Decompression for Osteonecrosis of Femoral Head

Published on: April 12, 2022

4.0K

Rupture Detection During Needle Insertion Using Complex OCT Data and CNNs.

Sarah Latus, Johanna Sprenger, Maximilian Neidhardt

    IEEE Transactions on Bio-Medical Engineering
    |March 2, 2021
    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

    Autopsy practices for high-consequence infectious diseases: global guidelines, alternatives, and the BSL-4 gap.

    Emerging microbes & infections·2026
    Same author

    Spatiotemporal distribution of SARS-CoV-2 vaccines and vaccine-related proteins in mice and humans.

    Scientific reports·2026
    Same author

    A review of deep learning-based Unsupervised Anomaly Detection in brain MRI.

    Medical image analysis·2026
    Same author

    A-scan sequence transformers for palpation with optical coherence elastography.

    Biomedical optics express·2026
    Same author

    Spatiotemporal remodeling of bone as a reversibly adaptive biological material in Djungarian hamsters under regulated photoperiod conditions.

    Acta biomaterialia·2026
    Same author

    Gender and Age-Related Decline in Lower Limb Standing Muscle Strength: Benchmarking for Rehabilitation Assessment.

    Sensors (Basel, Switzerland)·2026
    Same journal

    Highly Accelerated 1-mm Isotropic 3D Chemical Exchange Saturation Transfer MRI Using Wave-Co-CAIPI at 5 Tesla.

    IEEE transactions on bio-medical engineering·2026
    Same journal

    Systematic Evaluation of Hip Exoskeleton Assistance Parameters for Enhancing Gait Stability During Ground Slip Perturbations.

    IEEE transactions on bio-medical engineering·2026
    Same journal

    SleepConFormer: A Single-Channel EEG Framework for Sleep Staging and Consciousness Assessment in Patients with Disorders of Consciousness.

    IEEE transactions on bio-medical engineering·2026
    Same journal

    Modeling Partial and Total Support of Left Ventricular Assist Device for Discrete Hemodynamic Control Framework.

    IEEE transactions on bio-medical engineering·2026
    Same journal

    A Low-Cost Wearable TI-TACS Stimulator With Bipolar Quadratic-Boost Converter for Current Stimulation Validation in the Rat Brain.

    IEEE transactions on bio-medical engineering·2026
    Same journal

    EMG-Based Gait Estimation Using Koopman-Inspired Method.

    IEEE transactions on bio-medical engineering·2026
    See all related articles

    This study introduces a machine learning method to detect tissue ruptures using optical coherence tomography (OCT) during needle insertion. This approach enhances navigation accuracy without relying on external ultrasound or force sensors.

    Area of Science:

    • Medical imaging
    • Robotics
    • Machine Learning

    Background:

    • Soft tissue deformation and ruptures complicate accurate needle placement.
    • Tissue interfaces offer navigation cues, but ultrasound (US) guidance alignment and friction from external forces can be challenging.
    • Optical coherence tomography (OCT) provides high-resolution imaging at the needle tip but faces challenges with limited penetration depth and data interpretation.

    Purpose of the Study:

    • To develop and validate a machine learning approach for detecting tissue ruptures using OCT data during needle insertion.
    • To enable accurate navigation through tissue layers by identifying ruptures without relying on external guidance systems.
    • To improve the robustness of robotic needle placement through enhanced intra-procedural guidance.

    Main Methods:

    More Related Videos

    Cavernous Nerve Stimulation and Recording of Intracavernous Pressure in a Rat
    07:43

    Cavernous Nerve Stimulation and Recording of Intracavernous Pressure in a Rat

    Published on: April 23, 2018

    13.0K
    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
    04:23

    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

    Published on: April 21, 2023

    2.1K

    Related Experiment Videos

    Last Updated: Nov 15, 2025

    Augmented Reality Navigation-Guided Core Decompression for Osteonecrosis of Femoral Head
    06:17

    Augmented Reality Navigation-Guided Core Decompression for Osteonecrosis of Femoral Head

    Published on: April 12, 2022

    4.0K
    Cavernous Nerve Stimulation and Recording of Intracavernous Pressure in a Rat
    07:43

    Cavernous Nerve Stimulation and Recording of Intracavernous Pressure in a Rat

    Published on: April 23, 2018

    13.0K
    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
    04:23

    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

    Published on: April 21, 2023

    2.1K
    • An experimental setup was created for reproducible needle insertions.
    • Optical coherence tomography (OCT) was applied directly at the needle tip, complemented by external ultrasound (US) and force measurements for ground-truth data.
    • A machine learning model was trained on multi-modal ground-truth data to detect ruptures in complex OCT data.

    Main Results:

    • The machine learning approach achieved high accuracy in detecting ruptures: 0.94 on homogeneous phantoms, 0.91 on inhomogeneous phantoms, and 0.71 on ex-situ tissues.
    • Rupture detection was successful using only complex OCT data, without requiring additional external guidance or measurements after training.
    • The method demonstrated the ability to detect ruptures even in deeper tissue structures.

    Conclusions:

    • A deep learning-based method for rupture detection in OCT data was proposed, enabling navigation without external US or force sensors.
    • This approach effectively processes complex OCT data, overcoming limitations of penetration depth and interpretability.
    • The study presents a promising method to enhance robotic needle placement accuracy and reliability.