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 Experiment Video

Updated: May 7, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

373

A hybrid semi-automatic method for liver segmentation based on level-set methods using multiple seed points.

Xiaopeng Yang1, Hee Chul Yu, Younggeun Choi

  • 1Pohang University of Science and Technology, Pohang 790-784, South Korea.

Computer Methods and Programs in Biomedicine
|October 12, 2013
PubMed
Summary

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

Corrigendum to "Automatic liver Couinaud segmentation from computed tomography scans with a gradient-enhanced hierarchical cascade deep learning network" [Current Problems in Surgery 75 (2026) 101957].

Current problems in surgery·2026
Same author

[Changes in upper limb meridian detection and correlation analysis in patients with cervical radiculopathy].

Zhongguo gu shang = China journal of orthopaedics and traumatology·2026
Same author

Value of MR high-resolution vessel wall imaging in the Moyamoya-like collateral vessels diseases at the base of the brain.

Medicine·2026
Same author

Deep Learning-Based Liver Tumor Segmentation from Computed Tomography Scans with a Gradient-Enhanced Network.

Diagnostics (Basel, Switzerland)·2026
Same author

Automatic liver Couinaud segmentation from computed tomography scans with a gradient-enhanced hierarchical cascade deep learning network.

Current problems in surgery·2026
Same author

Glomerular filtration rate in patients with renal space-occupying masses using combination of SPECT <sup>99m</sup>Tc-DTPA renal dynamic imaging and contrast-enhanced CT.

Frontiers in oncology·2026
This summary is machine-generated.

A new hybrid semi-automatic method accurately extracts the liver from abdominal CT scans, improving segmentation for virtual liver surgery planning.

Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Surgical Planning

Background:

  • Accurate liver segmentation is crucial for preoperative planning in virtual liver surgery.
  • Existing methods may lack efficiency and precision in complex abdominal CT datasets.

Purpose of the Study:

  • To develop and evaluate a novel hybrid semi-automatic method for precise liver extraction from abdominal CT images.
  • To compare the performance and efficiency of the proposed method against traditional 2D region growing techniques.

Main Methods:

  • A hybrid approach combining a customized fast-marching level-set method with a threshold-based level-set method was developed.
  • The method utilizes user-selected seed points for initial liver region detection and subsequent refinement.
  • Performance was evaluated on abdominal CT datasets from 15 patients, comparing accuracy and time metrics.
Keywords:
Level-set methodLiver segmentationRegion growing methodSemi-automatic segmentationVirtual liver surgery planning

More Related Videos

Hybrid &#181;CT-FMT imaging and image analysis
13:45

Hybrid µCT-FMT imaging and image analysis

Published on: June 4, 2015

14.6K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.7K

Related Experiment Videos

Last Updated: May 7, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

373
Hybrid &#181;CT-FMT imaging and image analysis
13:45

Hybrid µCT-FMT imaging and image analysis

Published on: June 4, 2015

14.6K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.7K

Main Results:

  • The hybrid method achieved superior accuracy in liver extraction, evidenced by a higher similarity index (97.6%) and lower error rates (FPE 2.2%, FNE 2.5%).
  • Significantly reduced total extraction time (77s vs. 575s) and user interaction time (28s vs. 484s) were observed compared to the 2D region growing method.
  • The average symmetric surface distance was notably lower with the hybrid method (1.4mm vs. 6.7mm).

Conclusions:

  • The developed hybrid semi-automatic method offers a highly accurate and efficient solution for liver segmentation in abdominal CT images.
  • This technique is well-suited for enhancing preoperative planning in virtual liver surgery.
  • The method demonstrates significant advantages over conventional 2D region growing techniques in terms of accuracy and speed.