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

Deep Learning-Based Identification of Surgical Candidacy for Cervical Spinal Cord Decompression.

International journal of spine surgery·2026
Same author

Machine learning predicts cerebral vasospasm in patients with subarachnoid haemorrhage.

EBioMedicine·2024
Same author

Machine Learning Predicts Cerebral Vasospasm in Subarachnoid Hemorrhage Patients.

Research square·2024
Same author

Lumbar Spinal Canal Segmentation in Cases with Lumbar Stenosis Using Deep-U-Net Ensembles.

World neurosurgery·2023
Same author

Harmonization of multi-site functional connectivity measures in tangent space improves brain age prediction.

Proceedings of SPIE--the International Society for Optical Engineering·2023
Same author

Tau-Neurodegeneration <i>mismatch</i> reveals vulnerability and resilience to comorbidities in Alzheimer's continuum.

medRxiv : the preprint server for health sciences·2023
Same journal

Improving Reliability of MRI Lumbar Spinal Stenosis Assessment Across Radiology and Spine Specialties: Impact of a Structured Education Intervention.

Academic radiology·2026
Same journal

Advances in CT and MRI for Yttrium-90 Radioembolization of Hepatocellular Carcinoma.

Academic radiology·2026
Same journal

Homogeneity of Liver Fat Distribution Serves as a Diagnostic Marker for Metabolic Dysfunction-Associated Steatohepatitis.

Academic radiology·2026
Same journal

MRI-based Predictors and Risk Constellations of Chronic Ankle Instability After Acute Lateral Ankle Sprain: A Multicenter Study.

Academic radiology·2026
Same journal

Early Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer using a Longitudinal US-based Stack-model.

Academic radiology·2026
Same journal

Evaluating the Impact of Embolization on Outcomes in Iliopsoas Hematomas: A Multicenter Retrospective Propensity-matched Study.

Academic radiology·2026
See all related articles

Related Experiment Video

Updated: Apr 16, 2026

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

9.6K

Automated tumor volumetry using computer-aided image segmentation.

Bilwaj Gaonkar1, Luke Macyszyn1, Michel Bilello1

  • 1Department of Radiology, University of Pennsylvania, 3600 Market St, Suite 380, Philadelphia, Pennsylvania, 19104 (B.G., M.B., M.S.S., H.A., X.D., C.D.); Center for Biomedical Image Computing and Analytics (B.G., L.M., M.B., H.A., X.D., C.D.) and Department of Neurosurgery (L.M., M.A.A., Z.S.A., D.O.R., S.M.G.), University of Pennsylvania, Philadelphia, Pennsylvania; and Siemens Medical Solutions, Malvern, Pennsylvania (Y.Z.).

Academic Radiology
|March 16, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a fast, accurate, and robust semiautomatic method for brain tumor segmentation. It enables precise tumor volume quantification, addressing a critical need in neuro-oncology without manual segmentation.

Keywords:
Tumor segmentationgeodesic distancevolumetric analysis

More Related Videos

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

25.1K
Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

10.3K

Related Experiment Videos

Last Updated: Apr 16, 2026

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

9.6K
From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

25.1K
Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

10.3K

Area of Science:

  • Neuro-oncology
  • Medical imaging analysis
  • Computational anatomy

Background:

  • Accurate brain tumor segmentation and volume quantification are crucial for diagnosis, monitoring, and treatment planning.
  • Manual segmentation is time-consuming and not widely adopted.
  • Existing automated methods often lack robustness across different tumor types and imaging variations.

Purpose of the Study:

  • To develop and validate a semiautomatic method for brain tumor segmentation.
  • To provide a fast, accurate, and robust solution for tumor volume quantification.
  • To overcome limitations of existing methods in handling diverse tumor types and image qualities.

Main Methods:

  • A semiautomatic segmentation technique utilizing the geodesic distance transform was developed.
  • The method was validated on 54 brain tumors, including glioblastomas, meningiomas, and metastases.
  • Validation involved both qualitative assessment by clinical experts and quantitative comparison with manual segmentations.

Main Results:

  • Quantitative comparison using the Dice measure demonstrated strong agreement between semiautomatic and manual segmentations.
  • Expert ratings indicated high-quality computerized segmentations.
  • The method proved robust to variations in image quality and resolution.

Conclusions:

  • The proposed semiautomatic method fulfills a significant unmet need in neuro-oncology.
  • It allows for accurate and reproducible brain tumor volume quantification.
  • Clinicians can achieve reliable results without relying on manual segmentation.