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

Predictive dosimetric parameters for adaptive replanning in proton beam therapy for thoracic cancer.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists·2026
Same author

Deciphering small sequence differences in T cell receptor-antigen pairing.

Nature communications·2026
Same author

Executive summary of American Radium Society Appropriate Use Criteria for the treatment of locoregionally recurrent rectal cancer.

Cancer·2026
Same author

Association of Triglyceride-Glucose Index With In-Hospital Malignant Arrhythmias in Older Patients With Diabetes Mellitus and Left Ventricular Aneurysm: A Retrospective Study.

Reviews in cardiovascular medicine·2026
Same author

Palladium-Catalyzed Direct C(sp<sup>3</sup>)-H Arylation and Phenethylation of Toluenes with Aryl Iodides and Styrenes.

The Journal of organic chemistry·2026
Same author

Liver fibrosis biomarkers as a prognostic tool beyond the defining components in cardiovascular-kidney-metabolic syndrome.

American journal of preventive cardiology·2026

Related Experiment Video

Updated: Apr 5, 2026

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

14.9K

Automated compromised right lung segmentation method using a robust atlas-based active volume model with sparse shape

Jinghao Zhou1, Zhennan Yan2, Giovanni Lasio1

  • 1Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|August 11, 2015
PubMed
Summary

This study presents an automated framework for segmenting the right lung in oncologic patients, improving accuracy for compromised lungs. The new method offers better segmentation precision with reduced variability compared to existing techniques.

Keywords:
Atlas-based active volume modelCompromised lung segmentationImage segmentationLung cancerSparse shape composition

More Related Videos

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

2.2K
Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models
03:38

Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models

Published on: June 20, 2025

1.1K

Related Experiment Videos

Last Updated: Apr 5, 2026

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

14.9K
Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

2.2K
Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models
03:38

Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models

Published on: June 20, 2025

1.1K

Area of Science:

  • Medical imaging
  • Radiology
  • Computational anatomy

Background:

  • Accurate lung segmentation is crucial for oncologic patient management.
  • Severely compromised lungs in patients present significant segmentation challenges.
  • Existing atlas-based methods may lack precision in complex cases.

Purpose of the Study:

  • To develop an automated framework for robust right lung segmentation in oncologic patients.
  • To address segmentation difficulties in compromised lung structures.
  • To enhance segmentation accuracy and reduce variance compared to traditional methods.

Main Methods:

  • An automated right lung segmentation framework utilizing a robust, atlas-based active volume model.
  • Integration of sparse shape composition prior for enhanced atlas robustness.
  • Validation using thoracic computed tomography (CT) images from 38 lung tumor patients with manual segmentation as reference.

Main Results:

  • The proposed method achieved a mean Dice Similarity Coefficient (DSC) of (0.72, 0.81) with 95% CI.
  • High mean accuracy (ACC) of (0.97, 0.98) with 95% CI and low mean relative error (RE) of (0.46, 0.74) with 95% CI were observed.
  • Qualitative and quantitative analyses demonstrated superior segmentation accuracy and less variance than other atlas-based methods.

Conclusions:

  • The developed automated framework effectively segments the right lung, even in compromised cases.
  • The sparse shape composition prior significantly enhances atlas-based segmentation robustness.
  • This method shows promise for improved image analysis in thoracic oncology.