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

Increased early activation of CD56dimCD16dim/- natural killer cells in immunological non-responders correlates with CD4+ T-cell recovery.

Chinese medical journal·2020
Same author

In situ experimental measurement of mercury by combining PGNAA and characteristic X-ray fluorescence.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2020
Same author

Tris (1,3-dichloro-2-propyl) phosphate exposure disrupts the gut microbiome and its associated metabolites in mice.

Environment international·2020
Same author

Genome Resource of <i>Sphingomonas carotinifaciens</i> L9-754<sup>T</sup>, an Endophyte Isolated From Leaf Tissues of <i>Jatropha curcas</i>.

Plant disease·2020
Same author

Heterozygous <i>PGM3</i> Variants Are Associated With Idiopathic Focal Epilepsy With Incomplete Penetrance.

Frontiers in genetics·2020
Same author

An Inverse Dose Optimization Algorithm for Three-Dimensional Brachytherapy.

Frontiers in oncology·2020

Related Experiment Video

Updated: Nov 2, 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

3.1K

Deep learning applications in automatic segmentation and reconstruction in CT-based cervix brachytherapy.

Hai Hu1,2, Qiang Yang1,2, Jie Li2

  • 1Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu, China.

Journal of Contemporary Brachytherapy
|June 14, 2021
PubMed
Summary

A deep learning method accurately segments and reconstructs applicators in CT images for cervix brachytherapy, improving treatment planning efficiency and accuracy.

Keywords:
applicator segmentationbrachytherapycervical cancerdeep learningdosimetric comparison

More Related Videos

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions
06:18

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Published on: April 5, 2024

1.3K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.0K

Related Experiment Videos

Last Updated: Nov 2, 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

3.1K
Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions
06:18

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Published on: April 5, 2024

1.3K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.0K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiation Oncology

Background:

  • Cervix brachytherapy treatment planning requires accurate applicator reconstruction.
  • Manual reconstruction is time-consuming and prone to errors.
  • Deep learning offers potential for automated image analysis.

Purpose of the Study:

  • To investigate a deep learning method for automatic segmentation and reconstruction of applicators in CT images.
  • To evaluate the accuracy and efficiency of the deep learning method for cervix brachytherapy.
  • To assess the clinical feasibility of automated applicator localization.

Main Methods:

  • A U-Net model was developed for applicator segmentation in CT images.
  • Sixty patients' data were used for training and validation, with 10 for testing.
  • Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD95) were used for evaluation.

Main Results:

  • The deep learning model achieved an average DSC of 0.89 and HD95 of 1.66 mm.
  • Reconstruction time averaged 17.12 seconds with minimal tip and shaft errors.
  • Dosimetric differences between manual and automated reconstruction were clinically insignificant.

Conclusions:

  • A deep learning-based method for localizing Fletcher applicators in 3D CT images was developed.
  • The method demonstrated high accuracy and efficiency, making it clinically attractive.
  • This approach facilitates the automation of brachytherapy treatment planning.