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

Transition-Metal-Free Arylation and Alkylation of Diarylmethyl p-Tolyl Sulfones with Zinc Reagents.

The Journal of organic chemistry·2018
Same author

Germline mutations in PPFIBP2 are associated with lethal prostate cancer.

The Prostate·2018
Same author

High maternal osteocalcin levels during pregnancy is associated with low birth weight infants: A nested case-control study in China.

Bone·2018
Same author

The safety, tolerability, and pharmacokinetic profile of GSK2838232, a novel 2nd generation HIV maturation inhibitor, as assessed in healthy subjects.

Pharmacology research & perspectives·2018
Same author

Differences in inherited risk among relatives of hereditary prostate cancer patients using genetic risk score.

The Prostate·2018
Same author

Biallelic Mutations in MYORG Cause Autosomal Recessive Primary Familial Brain Calcification.

Neuron·2018

Related Experiment Video

Updated: Jul 11, 2026

Pioneering Patient-Specific Approaches for Precision Surgery Using Imaging and Virtual Reality
06:18

Pioneering Patient-Specific Approaches for Precision Surgery Using Imaging and Virtual Reality

Published on: April 5, 2024

[A novel validation method based on radial distance error for 3D medical image segmentation].

Jianfeng Xu1, Lixu Gu

  • 1Department of Computer and Engineering, Shanghai Jiaotong University, Shanghai 200240, China. xujf@sjtu.edu.cn

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|September 29, 2007
PubMed
Summary

This study introduces a novel validation method for 3D medical image segmentation, enhancing accuracy and intuition. The new approach, comprising local error and global accuracy validation, offers a more effective evaluation of segmentation algorithms.

More Related Videos

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

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Related Experiment Videos

Last Updated: Jul 11, 2026

Pioneering Patient-Specific Approaches for Precision Surgery Using Imaging and Virtual Reality
06:18

Pioneering Patient-Specific Approaches for Precision Surgery Using Imaging and Virtual Reality

Published on: April 5, 2024

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

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Area of Science:

  • Medical Imaging
  • Computer-Aided Surgery
  • Image Processing

Background:

  • 3D medical image segmentation is crucial for computer-assisted surgery and therapy.
  • Existing validation methods for segmentation lack efficiency and intuitiveness.
  • There is a need for improved evaluation techniques in medical image analysis.

Purpose of the Study:

  • To propose a new, efficient, and intuitive validation method for 3D medical image segmentation.
  • To introduce a hybrid segmentation approach and validate its performance.
  • To enhance the evaluation of segmentation algorithms in medical applications.

Main Methods:

  • A novel validation approach is detailed in two parts: local error validation and global accuracy validation.
  • The proposed method was applied to evaluate three standard segmentation algorithms.
  • A user-proposed hybrid segmentation method was also introduced and validated.

Main Results:

  • The experimental results demonstrated the effectiveness of the proposed validation method.
  • The new approach provided more accurate and intuitive evaluations of segmentation algorithms.
  • The hybrid segmentation method's performance was assessed using the novel validation technique.

Conclusions:

  • The proposed validation method offers a more accurate and intuitive evaluation of 3D medical image segmentation.
  • This new approach can improve the reliability of segmentation algorithms used in clinical practice.
  • The study highlights the potential of the new validation technique for advancing computer-assisted interventions.