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

4.2K
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...
4.2K

You might also read

Related Articles

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

Sort by
Same author

Tacrolimus (FK506) Attenuates Hepatic Ischemia-Reperfusion Injury via Oxidative Glutathione Metabolism and Suppression of Lipoxygenase-Mediated Cell Death.

Antioxidants (Basel, Switzerland)·2026
Same author

Expert Evaluation of the Perceived Accuracy, Relevance, and Safety of Large Language Model-Generated Patient Information in Geriatrics: Cross-Condition Study.

JMIR AI·2026
Same author

The Vitamin D3 Analog Calcipotriol Attenuates Pancreatic Cancer Malignancy via Downregulating Thrombospondin 1 in Pancreatic Stellate Cells.

Mediators of inflammation·2026
Same author

Impact of Hypothermic Oxygenated Machine Perfusion on Immune Cell Clearance in Liver Transplantation: Enhancing Graft Function and Post-Transplant Outcomes.

Journal of clinical medicine·2025
Same author

Surgical Outcome After Distal Pancreatectomy With and Without Portomesenteric Venous Resection in Patients with Pancreatic Adenocarcinoma: A Transatlantic Evaluation of Patients in North America, Germany, Sweden, and The Netherlands (GAPASURG).

Annals of surgical oncology·2024
Same author

Outcome of a 3-day vs 7-day selective digestive tract decontamination-based regimen for oral antibiotic bowel decontamination in left-sided colorectal surgery: A noninferiority study.

Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract·2024

Related Experiment Video

Updated: May 15, 2025

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
13:35

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos

Published on: March 21, 2021

10.3K

Validation of body composition parameters extracted via deep learning-based segmentation from routine computed

Felix O Hofmann1,2, Christian Heiliger3, Tengis Tschaidse3

  • 1Department of General, Visceral and Transplant Surgery, Ludwig-Maximilians-University Hospital Munich, Marchioninistrasse 15, 81377, Munich, Germany. Felix.Hofmann@med.uni-muenchen.de.

Scientific Reports
|April 8, 2025
PubMed
Summary

A new pipeline uses deep learning on CT scans to measure body composition, showing strong correlations with manual methods. Total skeletal muscle (SMtotal) offers stable, reliable measurements for predicting patient survival.

Keywords:
Body compositionComputed tomographyOncologySarcopeniaTissue segmentation

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

2.6K
Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography
07:33

Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography

Published on: November 8, 2024

311

Related Experiment Videos

Last Updated: May 15, 2025

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
13:35

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos

Published on: March 21, 2021

10.3K
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

2.6K
Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography
07:33

Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography

Published on: November 8, 2024

311

Area of Science:

  • Medical Imaging
  • Radiology
  • Oncology

Background:

  • Sarcopenia and body composition are critical patient outcome predictors.
  • Routine computed tomography (CT) scans contain valuable body composition data.
  • Accurate quantification of these metrics is essential for clinical decision-making.

Purpose of the Study:

  • To develop and validate an open-access deep learning pipeline for extracting body composition measures from CT scans.
  • To compare automated measurements with manual quantification.
  • To assess the prognostic value of body composition metrics for overall survival (OS).

Main Methods:

  • Developed a flexible, open-access pipeline integrating deep learning segmentation models.
  • Applied the pipeline to CT scans of 337 surgical oncology patients.
  • Quantified total skeletal muscle (SMtotal), psoas muscle (SMpsoas), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) manually and automatically.

Main Results:

  • Strong correlations observed between automated and manual measurements (SMpsoas: r=0.776, VAT: r=0.993, SAT: r=0.984; P<0.001).
  • Total skeletal muscle (SMtotal) demonstrated greater measurement stability across vertebral levels compared to psoas muscle (SMpsoas).
  • SMtotal and SMpsoas showed comparable performance in predicting overall survival (OS).

Conclusions:

  • The developed pipeline accurately extracts body composition measures from CT scans, correlating well with manual assessments.
  • Total skeletal muscle (SMtotal) offers a more stable and potentially reliable alternative to psoas muscle (SMpsoas) for patient outcome prediction.
  • Further refinement of automated segmentation models is recommended to address discrepancies and enhance accuracy.