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

Imaging studies for predicting hematoma expansion: from traditional imaging signs to artificial intelligence-based multimodal fusion.

Frontiers in neurology·2026
Same author

Gliosarcoma of the right cerebellar hemisphere and parahippocampal region: A case report and literature review.

Oncology letters·2026
Same author

Advancements in predicting clinical factors for hematoma enlargement in primary cerebral hemorrhage patients: a review.

European journal of medical research·2026
Same author

Shap-interpretable predictive modeling of microvascular invasion and early recurrence in hepatocellular carcinoma using MRI habitat imaging combined with clinical features.

European journal of radiology·2026
Same author

Analysis of the risk factors associated with complications following CT-guided percutaneous core needle biopsy of small pulmonary nodules (≤1.5 cm): A single-center retrospective study.

Medicine·2025
Same author

Precision Medicine in ICH Unveiling the Superior Predictive Power of a Joint Model.

Advanced biology·2025

Related Experiment Video

Updated: Mar 15, 2026

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

3.6K

Automatic 3D liver location and segmentation via convolutional neural network and graph cut.

Fang Lu1, Fa Wu1, Peijun Hu1

  • 1School of Mathematical Sciences, Zhejiang University, Hangzhou, 310027, China.

International Journal of Computer Assisted Radiology and Surgery
|September 9, 2016
PubMed
Summary

This study introduces an automated deep learning algorithm for liver segmentation in CT scans, achieving high accuracy for clinical applications. The method refines segmentation using graph cuts, offering a reproducible alternative to manual processes.

Keywords:
3D convolution neural networkCT imagesGraph cutLiver segmentation

More Related Videos

Three-dimensional Location Approach with Silk Thread Guided Laparoscopic Segmentectomy for Liver Tumor
06:39

Three-dimensional Location Approach with Silk Thread Guided Laparoscopic Segmentectomy for Liver Tumor

Published on: May 23, 2025

523
Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

255

Related Experiment Videos

Last Updated: Mar 15, 2026

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

3.6K
Three-dimensional Location Approach with Silk Thread Guided Laparoscopic Segmentectomy for Liver Tumor
06:39

Three-dimensional Location Approach with Silk Thread Guided Laparoscopic Segmentectomy for Liver Tumor

Published on: May 23, 2025

523
Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

255

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Accurate liver segmentation in computed tomography (CT) is crucial for clinical interventions like transplant planning and radiotherapy.
  • Manual segmentation is time-consuming and prone to reproducibility issues.

Purpose of the Study:

  • To develop a fully automatic deep learning algorithm for precise liver segmentation in abdominal CT scans.
  • To refine segmentation accuracy using graph cut techniques.

Main Methods:

  • A 3D convolutional neural network was employed for simultaneous liver detection and probabilistic segmentation.
  • Graph cut refinement was applied to enhance the accuracy of the initial deep learning segmentation.

Main Results:

  • The algorithm demonstrated high accuracy on public datasets (MICCAI-Sliver07 and 3Dircadb1), with low volumetric overlap error and surface distance metrics.
  • Quantitative results showed efficient and accurate hepatic volume estimation.

Conclusions:

  • The proposed method offers a fully automatic and accurate solution for liver segmentation in CT images.
  • This approach shows potential to replace manual segmentation, improving efficiency and reproducibility in clinical settings.