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

You might also read

Related Articles

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

Sort by
Same author

Impact of short-term temperature variability on emergency hospital admissions for schizophrenia stratified by season of birth.

International journal of biometeorology·2016
Same author

Design, Synthesis, and Evaluation of Thiophene[3,2-d]pyrimidine Derivatives as HIV-1 Non-nucleoside Reverse Transcriptase Inhibitors with Significantly Improved Drug Resistance Profiles.

Journal of medicinal chemistry·2016
Same author

Rationale and design of a randomized cluster trial to improve guideline-adherence of secondary preventive drugs prescription after coronary artery bypass grafting in China: Measurement and Improvement Studies of Surgical Coronary Revascularization: Secondary Prevention (MISSION-1) Study.

American heart journal·2016
Same author

Exenatide treatment increases serum irisin levels in patients with obesity and newly diagnosed type 2 diabetes.

Journal of diabetes and its complications·2016
Same author

AMPK-dependent regulation of GLP1 expression in L-like cells.

Journal of molecular endocrinology·2016
Same author

Multiprotein-bridging factor 1 regulates vegetative growth, osmotic stress, and virulence in Magnaporthe oryzae.

Current genetics·2016

Related Experiment Video

Updated: Jun 17, 2025

Bioelectric Analyses of an Osseointegrated Intelligent Implant Design System for Amputees
14:31

Bioelectric Analyses of an Osseointegrated Intelligent Implant Design System for Amputees

Published on: July 15, 2009

14.0K

Metal implant segmentation in CT images based on diffusion model.

Kai Xie1,2, Liugang Gao1,2, Yutao Zhang3,4

  • 1Radiotherapy Department, The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, 213000, China.

BMC Medical Imaging
|August 6, 2024
PubMed
Summary

A new diffusion model, DiffSeg, accurately segments metal implants in CT images, outperforming traditional methods. This advancement is crucial for improving metal artifact correction in medical imaging.

Keywords:
CTDiffusion modelMask segmentationMetal artifactMetal implant

More Related Videos

DUCT: Double Resin Casting followed by Micro-Computed Tomography for 3D Liver Analysis
12:39

DUCT: Double Resin Casting followed by Micro-Computed Tomography for 3D Liver Analysis

Published on: September 28, 2021

3.3K
Protocol for the Evaluation of MRI Artifacts Caused by Metal Implants to Assess the Suitability of Implants and the Vulnerability of Pulse Sequences
08:19

Protocol for the Evaluation of MRI Artifacts Caused by Metal Implants to Assess the Suitability of Implants and the Vulnerability of Pulse Sequences

Published on: May 17, 2018

9.8K

Related Experiment Videos

Last Updated: Jun 17, 2025

Bioelectric Analyses of an Osseointegrated Intelligent Implant Design System for Amputees
14:31

Bioelectric Analyses of an Osseointegrated Intelligent Implant Design System for Amputees

Published on: July 15, 2009

14.0K
DUCT: Double Resin Casting followed by Micro-Computed Tomography for 3D Liver Analysis
12:39

DUCT: Double Resin Casting followed by Micro-Computed Tomography for 3D Liver Analysis

Published on: September 28, 2021

3.3K
Protocol for the Evaluation of MRI Artifacts Caused by Metal Implants to Assess the Suitability of Implants and the Vulnerability of Pulse Sequences
08:19

Protocol for the Evaluation of MRI Artifacts Caused by Metal Implants to Assess the Suitability of Implants and the Vulnerability of Pulse Sequences

Published on: May 17, 2018

9.8K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Science

Background:

  • Computed tomography (CT) imaging is susceptible to artifacts caused by metal implants.
  • Accurate metal segmentation is essential for effective metal artifact correction.
  • Traditional threshold-based methods often provide insufficient accuracy for metal segmentation.

Purpose of the Study:

  • To develop and validate a diffusion model for precise metal implant segmentation in CT images.
  • To evaluate the model's performance on simulated, clinical artifact, and phantom datasets.
  • To enhance metal artifact reduction strategies through improved segmentation.

Main Methods:

  • A retrospective study involving 100 patients and simulated artifact data.
  • Utilized 11,280 slices for training/verification and 2,820 for testing.
  • Employed DiffSeg, a diffusion model with conditional dynamic coding and a global frequency parser (GFParser), for metal mask segmentation.

Main Results:

  • DiffSeg achieved 97.89% accuracy and 95.45% Dice Similarity Coefficient (DSC) on simulated data.
  • Outperformed classical deep learning networks and threshold segmentation (82.92%-84.19% DSC).
  • Demonstrated superior performance on clinical CT, artifact, and phantom data.

Conclusions:

  • DiffSeg offers efficient and robust metal mask segmentation in CT images, even with artifacts.
  • The model's architecture, incorporating conditional dynamic coding and GFParser, enhances segmentation accuracy.
  • Future integration into metal artifact reduction workflows is expected to improve overall image quality.