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

ASO Visual Abstract: Key Updates on the Version 9 AJCC/UICC Staging System for Salivary Gland Carcinoma.

Annals of surgical oncology·2026
Same author

Key Updates on the Version 9 AJCC/UICC Staging System for Salivary Gland Carcinoma.

Annals of surgical oncology·2026
Same author

Proposed Version Nine of the AJCC and UICC TNM Classification for Salivary Gland Carcinoma.

JAMA otolaryngology-- head & neck surgery·2026
Same author

High-Risk HPV Testing in Head and Neck Carcinoma: Key Updates from the 2025 College of American Pathologists Guideline.

Head and neck pathology·2025
Same author

Perceptions, Uses, and Information Sources of Medical Cannabis Among Patients With Cancer.

Advances in radiation oncology·2025
Same author

Human Papillomavirus Testing in Head and Neck Carcinomas: Guideline Update.

Archives of pathology & laboratory medicine·2025

Related Experiment Video

Updated: Oct 9, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.4K

Human-level comparable control volume mapping with a deep unsupervised-learning model for image-guided radiation

Xiaokun Liang1, Maxime Bassenne1, Dimitre H Hristov1

  • 1Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA.

Computers in Biology and Medicine
|December 23, 2021
PubMed
Summary

This study introduces a deep unsupervised learning method for precise patient positioning in head and neck cancer radiotherapy using control volume mapping. The new approach significantly improves registration accuracy compared to standard methods.

Keywords:
Head and neckImage registrationImage-guided radiation therapyPatient positioningUnsupervised learning

More Related Videos

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy
08:17

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Published on: June 7, 2015

15.9K
Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform
07:57

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

2.9K

Related Experiment Videos

Last Updated: Oct 9, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.4K
Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy
08:17

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Published on: June 7, 2015

15.9K
Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform
07:57

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

2.9K

Area of Science:

  • Medical Physics
  • Radiotherapy
  • Machine Learning

Background:

  • Accurate patient positioning is critical for effective radiotherapy.
  • Daily CT (dCT) and planning CT (pCT) scans are used for patient setup.
  • Image registration methods aim to align dCT with pCT for accurate positioning.

Purpose of the Study:

  • To develop a deep unsupervised learning method for precise patient positioning.
  • To utilize control volume (CV) mapping from dCT to pCT.
  • To automatically generate couch shifts (translation and rotation) for head and neck cancer (HNC) patients.

Main Methods:

  • Proposed an unsupervised learning framework mapping CVs from dCT to pCT.
  • Network inputs: dCT, pCT, and CV positions in pCT.
  • Trained network to maximize image similarity between CVs in dCT and pCT.
  • Evaluated on 554 CT scans from 158 HNC patients.

Main Results:

  • System positioning errors: translation < 0.47 mm, rotation < 0.17°.
  • Random positioning errors: translation < 1.13 mm, rotation < 0.29°.
  • Improved registration within tolerance (2.0 mm/1.0°) from 66.67% to 90.91%.

Conclusions:

  • Developed a deep unsupervised learning architecture for patient positioning using CV mapping.
  • Method mitigates image artifact influence by differential weighting of CV regions.
  • Achieved efficient and effective HNC patient positioning.