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

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...

You might also read

Related Articles

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

Sort by
Same author

Management of the Axilla for Early-Stage Breast Cancer: A State-Of-The-Art Review.

ANZ journal of surgery·2026
Same author

Splenic FDG PET uptake and CT volume as prognostic biomarkers in diffuse large B cell lymphoma.

La Radiologia medica·2026
Same author

Emergent domain segregation in self-interacting polymers explains chromosome 3D conformations in single human cells.

Physical review. E·2026
Same author

PSMA PET/CT staging in intermediate-risk prostate cancer: Toward risk-adapted implementation.

Seminars in oncology·2026
Same author

MiRInter-Trans: a transformer-based framework for microRNA interaction prediction.

Bioinformatics advances·2026
Same author

Overcoming absolute dysphagia in a thirty-year-old patient with advanced anaplastic lymphoma kinase-positive non-small cell lung cancer: a case report.

Frontiers in oncology·2026

Related Experiment Video

Updated: Jun 27, 2026

Hybrid µCT-FMT imaging and image analysis
13:45

Hybrid µCT-FMT imaging and image analysis

Published on: June 4, 2015

Liver segmentation from computed tomography scans: a survey and a new algorithm.

Paola Campadelli1, Elena Casiraghi, Andrea Esposito

  • 1Università degli Studi di Milano, Dipartimento di Scienze dell'Informazione, Via Comelico 39/41, 20135 Milano, Italy.

Artificial Intelligence in Medicine
|December 9, 2008
PubMed
Summary
This summary is machine-generated.

This study reviews liver segmentation techniques for computed tomography (CT) scans. A novel automated method shows promising results, comparable to human experts, addressing current challenges in medical image processing.

More Related Videos

Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease
08:41

Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease

Published on: March 24, 2023

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

Related Experiment Videos

Last Updated: Jun 27, 2026

Hybrid µCT-FMT imaging and image analysis
13:45

Hybrid µCT-FMT imaging and image analysis

Published on: June 4, 2015

Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease
08:41

Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease

Published on: March 24, 2023

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

Area of Science:

  • Medical Image Processing
  • Radiology

Background:

  • Liver segmentation from CT scans is crucial for automated liver disease diagnosis, volume measurement, and 3D rendering.
  • Accurate segmentation is a fundamental step in many clinical applications.

Purpose of the Study:

  • To review existing semi-automatic and automatic liver segmentation techniques.
  • To introduce and evaluate a novel fully automated liver segmentation method.

Main Methods:

  • A comprehensive review of current literature on liver segmentation.
  • Development and testing of a gray-level based automated segmentation algorithm.
  • Validation on a dataset of 40 patient CT scans.

Main Results:

  • Automatic liver segmentation remains an open challenge with existing methods having limitations.
  • The proposed automated method achieved satisfactory results.
  • Performance was comparable to mean intra- and inter-observer variability.

Conclusions:

  • The developed automated liver segmentation technique shows potential to overcome existing drawbacks.
  • Further validation with a standardized test set and performance measure is needed to confirm superiority.