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 Video

Updated: Jun 6, 2026

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy
09:11

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy

Published on: April 9, 2019

Semi-automatic segmentation for prostate interventions.

S Sara Mahdavi1, Nick Chng, Ingrid Spadinger

  • 1Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada. saram@ece.ubc.ca

Medical Image Analysis
|November 19, 2010
PubMed
Summary
This summary is machine-generated.

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

Towards Conversational AI for Disease Management.

Nature·2026
Same author

Self-supervised T2WI-bridged framework for liver segmentation and PDFF prediction from US images.

IEEE transactions on medical imaging·2026
Same author

Five-year follow-up of the SABR-5 trial: primary toxicity analysis of stereotactic ablative radiotherapy for up to five oligometastases.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology·2026
Same author

Advancing conversational diagnostic AI with multimodal reasoning.

Nature medicine·2026
Same author

Trends in brachytherapy utilization in Canada from 2011 to 2020.

Physics and imaging in radiation oncology·2026
Same author

A Case-Based Guide: Demonstrating the Reirradiation Collaborative Group (ReCOG) Consensus for Dosimetric Assessment and Reporting in Reirradiation.

Practical radiation oncology·2026

This study introduces a semi-automatic method for prostate segmentation in brachytherapy, significantly reducing contouring time and subjectivity. The approach uses a warped ellipsoid model, achieving accurate prostate delineation for improved treatment planning.

Area of Science:

  • Medical Imaging
  • Radiation Oncology
  • Computational Anatomy

Background:

  • Accurate prostate segmentation is crucial for effective brachytherapy.
  • Manual segmentation is time-consuming and prone to inter-observer variability.
  • Existing methods may lack the speed and consistency required for routine clinical use.

Purpose of the Study:

  • To develop and characterize a semi-automatic prostate segmentation method for brachytherapy.
  • To improve the efficiency and consistency of prostate gland delineation in ultrasound images.
  • To reduce subjectivity in segmentation compared to manual methods.

Main Methods:

  • A semi-automatic method using a warped and tapered ellipsoid as an a-priori 3D prostate shape.
  • Transforming endorectal transverse images into ellipses to solve the shape fitting problem as a convex optimization problem.

More Related Videos

Retzius-Sparing Robot-Assisted Radical Prostatectomy
12:10

Retzius-Sparing Robot-Assisted Radical Prostatectomy

Published on: May 19, 2022

Related Experiment Videos

Last Updated: Jun 6, 2026

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy
09:11

Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy

Published on: April 9, 2019

Retzius-Sparing Robot-Assisted Radical Prostatectomy
12:10

Retzius-Sparing Robot-Assisted Radical Prostatectomy

Published on: May 19, 2022

  • Validation through comparison with manual contours and assessment of segmentation time and observer variability.

    • Main Results:

    • Achieved an average whole gland error of 6.63 ± 0.9% compared to manual contours.
    • Reported a whole gland volume error of 5.82 ± 4.15% after physician modification.
    • Reduced segmentation inter- and intra-observer variability from 4.65% and 5.95% to 3.04% and 3.48%, respectively.
    • Total segmentation time, including initialization and potential modification, is under 4 minutes, significantly faster than manual methods.

    Conclusions:

    • The semi-automatic segmentation method is fast, consistent, and accurate for prostate delineation in ultrasound images.
    • The method reduces subjectivity and observer variability in segmentation.
    • It is successfully integrated into the standard treatment routine for low dose rate prostate brachytherapy at the British Columbia Cancer Agency.