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

Self-Awareness and Its Effects01:21

Self-Awareness and Its Effects

317
Self-awareness is a psychological state in which the individual becomes the focal point of their attention. This inward focus transforms the self into an object of contemplation and assessment, influencing how individuals perceive their actions and their alignment with personal and societal standards.Triggers and Contexts for Self-AwarenessSelf-awareness can be activated by external stimuli that make individuals visually or audibly aware of themselves, such as mirrors, cameras, or recordings.
317
Altered States of Awareness01:06

Altered States of Awareness

1.2K
Altered states of consciousness represent significant deviations from one's normal mental state. These deviations can range from subtle changes in awareness to profound transformations in perception, thought processes, and sensory experiences. Altered states of consciousness can be triggered by various factors, including drug use, meditation, hypnosis, illness, or even intense fatigue.
The ingestion of substances like stimulants or hallucinogens leads to chemical alterations in the brain...
1.2K
Subconsciousness and No Awareness01:15

Subconsciousness and No Awareness

715
The concept of subconscious awareness refers to the processing of information below the level of conscious thought, which significantly influences both behaviors and decisions. It is also known as waking subconscious awareness. This complex level of cognition operates without the direct awareness of the individual, facilitating rapid and simultaneous handling of multiple information streams.
An illustrative example of subconscious processing is its role in problem-solving. Often, individuals...
715
High-Level and Low-Level Awareness01:19

High-Level and Low-Level Awareness

784
Controlled processes in human consciousness represent high-alert mental states where individuals deliberately focus their attention on achieving specific goals. Controlled processes can be seen in situations like mastering new technology, where a person might become so absorbed that they ignore surrounding distractions. Such processes involve selective attention, requiring one to concentrate on particular elements of experience while disregarding others. These are governed by executive...
784
Self Within Cultural Contexts01:30

Self Within Cultural Contexts

240
Cultural frameworks for understanding the self are often categorized into two broad orientations: individualism and collectivism. These paradigms influence how people define themselves, relate to others, and interpret their social worlds. Each orientation offers distinct perspectives on autonomy, responsibility, and the role of the individual within a community.Individualistic CulturesIn individualistic cultures like North America and Western Europe, identity is understood as autonomous and...
240
Protein Networks02:26

Protein Networks

4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K

You might also read

Related Articles

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

Sort by
Same author

Segmentation-Based vs. Regression-Based Biomarker Estimation: A Case Study of Fetus Head Circumference Assessment from Ultrasound Images.

Journal of imaging·2022
Same author

Corrections to "Medical Image Synthesis With Deep Convolutional Adversarial Networks".

IEEE transactions on bio-medical engineering·2020
Same author

Multiorgan segmentation using distance-aware adversarial networks.

Journal of medical imaging (Bellingham, Wash.)·2019
Same author

Fully automated esophagus segmentation with a hierarchical deep learning approach.

Conference proceedings. IEEE International Conference on Signal and Image Processing Applications·2018
Same author

Medical Image Synthesis with Deep Convolutional Adversarial Networks.

IEEE transactions on bio-medical engineering·2018
Same author

Joint Segmentation of Multiple Thoracic Organs in CT Images with Two Collaborative Deep Architectures.

Deep learning in medical image analysis and multimodal learning for clinical decision support : Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, held in conjunction with MICCAI 2017 Quebec City, QC,...·2018

Related Experiment Video

Updated: Feb 7, 2026

Erythrocyte Sedimentation Rate: A Physics-Driven Characterization in a Medical Context
08:07

Erythrocyte Sedimentation Rate: A Physics-Driven Characterization in a Medical Context

Published on: March 24, 2023

4.0K

Medical Image Synthesis with Context-Aware Generative Adversarial Networks.

Dong Nie1,2, Roger Trullo1,3, Jun Lian4

  • 1Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|July 17, 2018
PubMed
Summary

Researchers developed a new method to create computed tomography (CT) images from magnetic resonance imaging (MRI) scans. This technique avoids radiation exposure, offering a safer alternative for radiation treatment planning.

Keywords:
Auto-contextDeep learningGANGenerative modelsImage synthesis

More Related Videos

Wideband Optical Detector of Ultrasound for Medical Imaging Applications
08:21

Wideband Optical Detector of Ultrasound for Medical Imaging Applications

Published on: May 11, 2014

11.8K
Generation of Shear Adhesion Map Using SynVivo Synthetic Microvascular Networks
09:52

Generation of Shear Adhesion Map Using SynVivo Synthetic Microvascular Networks

Published on: May 25, 2014

9.3K

Related Experiment Videos

Last Updated: Feb 7, 2026

Erythrocyte Sedimentation Rate: A Physics-Driven Characterization in a Medical Context
08:07

Erythrocyte Sedimentation Rate: A Physics-Driven Characterization in a Medical Context

Published on: March 24, 2023

4.0K
Wideband Optical Detector of Ultrasound for Medical Imaging Applications
08:21

Wideband Optical Detector of Ultrasound for Medical Imaging Applications

Published on: May 11, 2014

11.8K
Generation of Shear Adhesion Map Using SynVivo Synthetic Microvascular Networks
09:52

Generation of Shear Adhesion Map Using SynVivo Synthetic Microvascular Networks

Published on: May 25, 2014

9.3K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare

Background:

  • Computed Tomography (CT) is vital for radiation treatment planning but involves radiation exposure.
  • Magnetic Resonance Imaging (MRI) offers a safer, radiation-free alternative.
  • There is a clinical need to generate CT images from MRI data.

Purpose of the Study:

  • To develop a data-driven method for synthesizing CT images from corresponding MRI scans.
  • To improve the accuracy and realism of CT image generation from MRI data.
  • To provide a radiation-free approach for applications like radiation treatment planning.

Main Methods:

  • Training a fully convolutional network (FCN) for MRI-to-CT image generation.
  • Employing adversarial training strategies to enhance image realism and model nonlinear mappings.
  • Utilizing an image-gradient-difference loss function to reduce blurriness in generated CT images.
  • Implementing an Auto-Context Model (ACM) for a context-aware generative adversarial network.

Main Results:

  • The proposed method accurately and robustly predicts CT images from MR images.
  • The approach outperforms three existing state-of-the-art methods in CT image prediction.
  • Generated CT images exhibit reduced blurriness and improved realism compared to baseline methods.

Conclusions:

  • The developed data-driven approach effectively generates CT images from MRI data.
  • Adversarial training and specialized loss functions are crucial for high-quality CT synthesis.
  • This radiation-free method holds significant potential for clinical applications such as radiation therapy planning.