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

Masking and Demasking Agents01:19

Masking and Demasking Agents

2.6K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
2.6K

You might also read

Related Articles

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

Sort by
Same author

CrossModalSync: joint temporal-spatial fusion for semantic scene segmentation in large-scale scenes.

Scientific reports·2025
Same author

Enhancing generalization of medical image segmentation via game theory-based domain selection.

Journal of biomedical informatics·2025
Same author

Learning generalizable visual representation via adaptive spectral random convolution for medical image segmentation.

Computers in biology and medicine·2024
Same author

3D-DGGAN: A Data-Guided Generative Adversarial Network for High Fidelity in Medical Image Generation.

IEEE journal of biomedical and health informatics·2024
Same author

Volumetric Imitation Generative Adversarial Networks for Anatomical Human Body Modeling.

Bioengineering (Basel, Switzerland)·2024
Same author

Generalizable Polyp Segmentation via Randomized Global Illumination Augmentation.

IEEE journal of biomedical and health informatics·2024
Same journal

Correction: Komatsu et al. Three-Dimensional Visualization and Detection of the Pulmonary Venous-Left Atrium Connection Using Artificial Intelligence in Fetal Cardiac Ultrasound Screening. <i>Bioengineering</i> 2026, <i>13</i>, 100.

Bioengineering (Basel, Switzerland)·2026
Same journal

Comparison of CO<sub>2</sub> Laser and Microdebrider in the Surgical Treatment of Pediatric Recurrent Respiratory Papillomatosis: A Retrospective Analysis.

Bioengineering (Basel, Switzerland)·2026
Same journal

Toward More Translational Tumor Models: Breast dECM-Based 3D Systems Capture Native Microenvironmental Cues.

Bioengineering (Basel, Switzerland)·2026
Same journal

Postural Stability Changes During the 4 Phases of the Half Squat: Kinematics Profile of the Center of Pressure and Center of Mass in High-Performance Weightlifters-A Pilot Study.

Bioengineering (Basel, Switzerland)·2026
Same journal

Definite Implant Position as Novel Readout for Effectiveness of Ridge Preservation Indicates to Beneficial Effect of Combined Treatment with Platelet-Rich Fibrin (PRF) and Xenogenic Biomaterial in Bone Regeneration.

Bioengineering (Basel, Switzerland)·2026
Same journal

Trueness and Precision of Intraoral Scanners for 3D-Printed Orthodontic Models with Attachments: An In Vitro Comparative Study.

Bioengineering (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Aug 16, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.9K

Robust Medical Image Colorization with Spatial Mask-Guided Generative Adversarial Network.

Zuyu Zhang1, Yan Li1, Byeong-Seok Shin1

  • 1Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Republic of Korea.

Bioengineering (Basel, Switzerland)
|December 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel spatial mask-guided generative adversarial network (SMCGAN) for medical image colorization. SMCGAN effectively reduces artifacts by focusing on foreground regions, improving diagnostic visualization.

Keywords:
generative adversarial networkimage colorizationmedical images

More Related Videos

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

9.0K

Related Experiment Videos

Last Updated: Aug 16, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.9K
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

9.0K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Color medical images offer superior diagnostic information compared to grayscale.
  • Existing generative adversarial network (GAN) methods for colorization produce artifacts due to whole-image training.

Purpose of the Study:

  • To develop an automatic framework for artifact-free medical image colorization.
  • To enhance the visualization and diagnostic utility of medical images.

Main Methods:

  • Proposed a spatial mask-guided colorization with generative adversarial network (SMCGAN) framework.
  • Introduced an auxiliary foreground segmentation branch to generate spatial masks.
  • Employed two discriminators for generated and masked colorized images to focus on foreground regions.

Main Results:

  • SMCGAN achieved an average improvement of 8.48% in the Peak Signal-to-Noise Ratio (PSNR) metric.
  • Demonstrated superior performance over state-of-the-art GAN-based colorization approaches.
  • Generated colorized medical images with significantly fewer artifacts.

Conclusions:

  • The proposed SMCGAN framework effectively reduces artifacts in medical image colorization.
  • SMCGAN enhances the quality and diagnostic value of colorized medical images.
  • This method offers a promising approach for improving medical image visualization.