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 Videos

A general framework for context-specific image segmentation using reinforcement learning.

Lichao Wang1, Karim Lekadir, Su-Lin Lee

  • 1Hamlyn Centre for Robotic Surgery, Imperial College London, SW7 2AZ London, UK.

IEEE Transactions on Medical Imaging
|March 20, 2013
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...

You might also read

Related Articles

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

Sort by
Same author

Reconstruction from multi-planar MRI with foundation models for uterine fibroid analysis.

NPJ digital medicine·2026
Same author

Lignanamides, alkaloids, and other constituents from stems of <i>Tinospora crispa</i>.

Natural product research·2026
Same author

Brain Magnetic Resonance Elastography Experiments With an Electromagnetic Actuator.

Current protocols·2026
Same author

Magnetically Guided Microrobots for Targeted Drug Delivery.

Advanced healthcare materials·2026
Same author

Multiscale measurement of brain tissue and cell biomechanics using a mouse model.

Biophysics reports·2026
Same author

Lanostane-type triterpenoids from the fungus <i>Inonotus obliquus</i>.

Natural product research·2025
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026
Same journal

4D Reconstruction of Fetal Left Ventricle from Echocardiography via 2.5D Radial Segmentation and Graph-Fourier Reconstruction.

IEEE transactions on medical imaging·2026
Same journal

Generalised Medical Phrase Grounding.

IEEE transactions on medical imaging·2026
Same journal

EndoLRMGS: Combining Large Reconstruction Modelling and Gaussian Splatting for Complete Endoscopic Scene Reconstruction.

IEEE transactions on medical imaging·2026
Same journal

A Neural-Analytical Fusion Scatter Correction Method for Multi-Source CT Using Equivalent High-Order Scatter.

IEEE transactions on medical imaging·2026
See all related articles

This study introduces an adaptive online reinforcement learning framework for medical image segmentation. It reduces user interaction while maintaining accuracy by learning user intent in situ.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Machine Learning

Background:

  • Medical image segmentation is crucial for diagnosis and treatment planning.
  • Current methods often require significant user interaction and lack adaptability.
  • Integrating user intent and prior knowledge into segmentation models remains a challenge.

Purpose of the Study:

  • To develop an online reinforcement learning framework for adaptive medical image segmentation.
  • To introduce the concept of context-specific segmentation that incorporates user intention.
  • To reduce user interaction and improve segmentation accuracy and consistency.

Main Methods:

  • An online reinforcement learning framework was developed for medical image segmentation.
  • The framework incorporates context-specific segmentation, adapting to user intention and prior knowledge.

Related Experiment Videos

  • An implicit model was established for a large state-action space, enabling in situ learning.
  • The method was applied to four diverse medical image segmentation problems.

    • Main Results:

    • The proposed framework significantly reduced user interaction compared to traditional methods.
    • Segmentation accuracy and consistency were maintained or improved.
    • The method demonstrated generalizability across different image contents and segmentation requirements.
    • Validation confirmed the practical value and effectiveness of the adaptive approach.

    Conclusions:

    • The online reinforcement learning framework offers an adaptive and efficient solution for medical image segmentation.
    • Incorporating user intention through context-specific segmentation enhances model performance and usability.
    • The framework has the potential to streamline clinical workflows and improve diagnostic outcomes.