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

Editorial: Intelligent micro- and nanorobotic systems for enhanced medical procedures.

Frontiers in robotics and AI·2026
Same author

A Novel Eye- and Head-Tracking Interface for Digital Pathology Reduces Hand Movement and Improves Target Detection Accuracy.

Archives of pathology & laboratory medicine·2026
Same author

Deep learning for synovial volume segmentation of the first carpometacarpal joint in osteoarthritis patients.

Osteoarthritis imaging·2026
Same author

Endoscopic Closure of a Postablation Colorenal Fistula Using a Through-The-Scope Helical Suturing System.

ACG case reports journal·2026
Same author

Domain-agnostic Unsupervised Domain Adaptation Segmentation from 3D Carotid Artery Ultrasound Image.

IEEE journal of biomedical and health informatics·2026
Same author

Outcomes of Retrograde Transileal Ureteric Stents for Chronic Urinary Drainage: Comparison with Chronic Nephrostomy Tube Drainage.

Journal of vascular and interventional radiology : JVIR·2026

Related Experiment Video

Updated: Mar 11, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

243

Postediting prostate magnetic resonance imaging segmentation consistency and operator time using manual and

Maysam Shahedi1, Derek W Cool2, Cesare Romagnoli3

  • 1London Regional Cancer Program, 790 Commissioners Road, London, Ontario N6A 4L6, Canada; University of Western Ontario, Robarts Research Institute, 1151 Richmond Street, London, Ontario N6A 5B7, Canada; University of Western Ontario, Graduate Program in Biomedical Engineering, 1151 Richmond Street, London, Ontario N6A 3K7, Canada.

Journal of Medical Imaging (Bellingham, Wash.)
|November 23, 2016
PubMed
Summary

Computer-assisted prostate segmentation on T2w MRI reduces interoperator variability and editing time compared to manual methods. Direct measurement of editing time is crucial for evaluating clinical translation suitability of these algorithms.

Keywords:
editing timeimage segmentationmagnetic resonance imagingobserver studyprostaterepeatability

More Related Videos

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures
12:30

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

Published on: July 2, 2014

21.0K
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

9.6K

Related Experiment Videos

Last Updated: Mar 11, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

243
A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures
12:30

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

Published on: July 2, 2014

21.0K
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

9.6K

Area of Science:

  • Medical Imaging
  • Radiology
  • Computational Anatomy

Background:

  • Prostate segmentation on T2w MRI is critical for prostate cancer diagnosis and treatment.
  • Manual segmentation is inefficient and prone to significant interobserver variability.

Purpose of the Study:

  • To evaluate the clinical translation suitability of computer-assisted segmentation algorithms.
  • To compare interoperator variability and editing time across manual, semiautomatic, and automatic segmentation methods.

Main Methods:

  • A multioperator pilot study assessed manual, semiautomatic, and automatic prostate segmentation on T2w MRI.
  • Editing time and spatial error metrics were recorded for each segmentation method.
  • Interoperator variability was measured under pre- and postediting conditions.

Main Results:

  • Semiautomatic and automatic segmentation methods showed reduced interoperator variability compared to manual segmentation.
  • Average editing times were 213s (manual), 328s (semiautomatic), and 393s (automatic), with fully manual taking 564s.
  • No strong correlation was found between editing time and standard spatial error metrics.

Conclusions:

  • Computer-assisted segmentation offers potential for faster, more consistent prostate segmentation.
  • Direct measurement of post-segmentation editing time is essential for assessing clinical applicability.
  • Current error metrics may not fully capture the practical efficiency of segmentation algorithms.