Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Computed Tomography

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

Premature ventricular complexes.

Clinical medicine (London, England)·2026

Same author

Computational analysis of intracardiac collision risk and optimal site for right ventricular leadless left bundle branch area pacing: A simulation study.

Heart rhythm O2·2026

Same author

Cost-effectiveness modelling of early lead extraction for cardiac implantable electronic device infections in the United Kingdom.

BMJ open·2026

Same author

Laser vs rotational transvenous lead extraction: A systematic review and meta-analysis of procedural safety and efficacy outcomes.

Heart rhythm O2·2026

Same author

Risk stratification in transvenous lead extraction: Current models and clinical applications.

Heart rhythm O2·2026

Same author

Safety Outcomes of Hybrid Open Chest Transvenous Lead Extraction: A Multicenter Experience.

Pacing and clinical electrophysiology : PACE·2025

Search research articles

Related Experiment Video

Updated: Sep 17, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Catheter detection and segmentation in X-ray images via multi-task learning.

Lin Xi¹, Yingliang Ma^2,3, Ethan Koland⁴

¹School of Computing Sciences, University of East Anglia, Norwich, NR4 7TJ, UK. l.xi@uea.ac.uk.

International Journal of Computer Assisted Radiology and Surgery

|June 27, 2025

Summary

This study introduces a deep learning model for real-time detection and segmentation of surgical catheters in X-ray images. The method enhances surgical guidance by accurately locating devices during minimally invasive heart procedures.

Keywords:

Catheter detection Deep learning Multi-task learning Object segmentation X-ray fluoroscopy

More Related Videos

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Related Experiment Videos

Last Updated: Sep 17, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Minimally invasive heart surgeries require precise guidance.
Surgical devices like catheters need accurate localization in X-ray fluoroscopic images.
Automated detection and segmentation can improve image guidance.

Purpose of the Study:

To develop a real-time convolutional neural network (CNN) model for surgical device localization and segmentation.
To integrate electrode detection and catheter segmentation into an end-to-end deep learning framework.
To enhance image guidance in minimally invasive cardiac procedures.

Main Methods:

A CNN model with a ResNet architecture and multiple prediction heads was developed.
A multi-task learning strategy was employed for simultaneous electrode detection and catheter segmentation.
A novel multi-level dynamic resource prioritization method was introduced to balance task difficulty during training.

Main Results:

The method was validated on public and private datasets for single-task and multi-task scenarios.
Significant improvements were demonstrated compared to state-of-the-art methods.
Achieved a mean J of 64.37/63.97 for detection and segmentation multi-task on validation and test sets.

Conclusions:

The proposed approach offers a balance between accuracy and efficiency.
The model is suitable for real-time surgical guidance applications.
Automated detection and segmentation of surgical devices can enhance minimally invasive procedures.