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

Computed Tomography01:10

Computed Tomography

5.7K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
5.7K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

44
DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
44
Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

391
Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
391
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

427
Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
427

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Efficient Synthesis of N-Bridged Annulenes.

Journal of the American Chemical Society·2026
Same author

TriCD-Net: Triple-Attention Coordinated Cross-layer Dynamic Network for Few-Shot Medical Image Segmentation.

IEEE transactions on medical imaging·2026
Same author

Intra-fractional Voxel-wise Anatomical Motion Tracking Guided by Multimodal Respiratory Surrogates in Radiotherapy: Framework Development and Multi-Center Validation.

International journal of radiation oncology, biology, physics·2026
Same author

Neoadjuvant retlirafusp alfa (anti-PD-L1/TGF-β bifunctional fusion protein) with or without chemotherapy in unresectable stage III non-small cell lung cancer: updated results from the phase 2 TRAILBLAZER trial.

Signal transduction and targeted therapy·2026
Same author

Multimodal voxel-wise of structural and functional brain alterations across the cerebral small vessel disease spectrum.

Neuroradiology·2026
Same author

Sintilimab plus concurrent chemoradiotherapy for treatment of locally advanced small cell lung cancer (SINCE-01): a phase II clinical trial.

Signal transduction and targeted therapy·2026

Related Experiment Video

Updated: Sep 2, 2025

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

14.3K

Development and verification of radiomics framework for computed tomography image segmentation.

Jiabing Gu1,2, Baosheng Li1,2, Huazhong Shu1

  • 1Southeast University, Laboratory of Image Science and Technology, Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, Centre de Recherche en Information Biomédicale Sino-français (CRIBs), Nanjing, P. R. China.

Medical Physics
|August 2, 2022
PubMed
Summary

This study developed a radiomics-based framework for image segmentation (RFIS) using reproducible features to classify subvolumes. RFIS effectively segments gross target volumes by merging subvolumes with similar quantitative image information.

Keywords:
computed tomographyimage segmentationradiomicstumor

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

2.9K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

9.3K

Related Experiment Videos

Last Updated: Sep 2, 2025

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

14.3K
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

2.9K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

9.3K

Area of Science:

  • Medical Imaging
  • Radiomics
  • Computational Pathology

Background:

  • Radiomics offers quantitative image information (QII) for medical imaging.
  • Integrating radiomics into image segmentation is challenging but desirable.

Purpose of the Study:

  • Develop and validate a radiomics-based framework for image segmentation (RFIS).
  • Assess RFIS feasibility for gross target volume (GTV) segmentation in lung cancer.

Main Methods:

  • Extracted 53 subvolume features (svfeatures) from sliding window subvolumes (swvolumes).
  • Utilized isolation forest for outlier detection and %COV for reproducibility assessment.
  • Employed support vector machine for classification, tuned via 10-fold cross-validation, and applied mode filtering for final segmentation.

Main Results:

  • Achieved high reproducibility for 92.45% of svfeatures (%COV<15).
  • RFIS demonstrated strong performance with cross-validation AUC of 0.906 and test set AUC of 0.877.
  • Mean Dice similarity coefficient (DSC) for GTV segmentation was 0.707 in training and 0.688 in test sets.

Conclusions:

  • Reproducible radiomic features effectively capture QII differences for subvolume classification.
  • RFIS enables image segmentation by grouping and merging subvolumes with similar QII.