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

Analytics Methodology to Quantify MRI Exam Utilization.

Journal of imaging informatics in medicine·2026
Same author

Automated Delineation of Couinaud Segments at CT for Future Liver Remnant Volumetry.

Radiology. Artificial intelligence·2026
Same author

AI-driven credibility profiling of real-world patient experiences suggests overlooked kidney stone therapies warrant further investigation.

Research square·2026
Same author

CT-based Opportunistic Screening for Adding Clinical Value: How I Do It.

Radiology·2026
Same author

Critical Temperature Thresholds for Identifying Vulnerability to Heat-Related Excess Cardiovascular Morbidity and Mortality.

Journal of the American Heart Association·2026
Same author

Budd-Chiari Syndrome: Update on Classification and Intravascular US.

Radiographics : a review publication of the Radiological Society of North America, Inc·2025

Related Experiment Video

Updated: Oct 15, 2025

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
06:09

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Published on: July 21, 2023

1.4K

Deep Learning CT-based Quantitative Visualization Tool for Liver Volume Estimation: Defining Normal and Hepatomegaly.

Alberto A Perez1, Victoria Noe-Kim1, Meghan G Lubner1

  • 1From the Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252 (A.A.P., V.N.K., M.G.L., P.M.G., J.W.G., P.J.P.); and Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (D.C.E., R.M.S.).

Radiology
|October 26, 2021
PubMed
Summary

A new deep learning tool accurately measures liver volume using CT scans, establishing a weight-based threshold for hepatomegaly. This AI-driven approach offers a more objective assessment than traditional linear measurements for diagnosing liver enlargement.

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

1.3K
Author Spotlight: A Non-Invasive Tool to Assess and Differentiate Fat Patterns in Liver Using 3D Dixon MRI
05:37

Author Spotlight: A Non-Invasive Tool to Assess and Differentiate Fat Patterns in Liver Using 3D Dixon MRI

Published on: October 20, 2023

1.7K

Related Experiment Videos

Last Updated: Oct 15, 2025

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
06:09

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI

Published on: July 21, 2023

1.4K
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

1.3K
Author Spotlight: A Non-Invasive Tool to Assess and Differentiate Fat Patterns in Liver Using 3D Dixon MRI
05:37

Author Spotlight: A Non-Invasive Tool to Assess and Differentiate Fat Patterns in Liver Using 3D Dixon MRI

Published on: October 20, 2023

1.7K

Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Current imaging methods for hepatomegaly lack precise definition and rely on suboptimal unidimensional measurements.
  • Liver volume offers a more direct and accurate metric for assessing organ enlargement.

Purpose of the Study:

  • To determine liver organ volume using a validated deep learning artificial intelligence (AI) tool.
  • To establish reliable thresholds for diagnosing hepatomegaly based on automated liver segmentation.

Main Methods:

  • Retrospective analysis of multidetector CT scans from 3065 asymptomatic adults undergoing colorectal cancer screening or renal donor evaluation.
  • Utilized a deep learning tool for automated liver segmentation and volume derivation, with standardization for unenhanced scans.
  • Assessed linear measures (craniocaudal, 3D) and compared automated volumes with manual measurements in a subset of patients.

Main Results:

  • Automated liver volume averaged 1533 mL ± 375, with patient weight identified as the primary determinant.
  • A novel weight-based upper limit of normal hepatomegaly threshold was derived: hepatomegaly (mL) = 14.0 × (weight [kg]) + 979.
  • The AI tool demonstrated high accuracy, with a median volume difference of 2.3% compared to manual methods.

Conclusions:

  • Automated CT-based liver volume segmentation using deep learning provides an objective and more accurate assessment of liver size.
  • The developed weight-based threshold offers a superior method for diagnosing hepatomegaly compared to traditional linear measurements.