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

Evaluating Diagnostic Accuracy and Inter-reader Agreement of the Prostate Imaging After Focal Ablation Scoring System.

European urology open science·2024
Same author

A Phase 1 Trial of Salvage Stereotactic Body Radiation Therapy for Radiorecurrent Prostate Cancer After Brachytherapy.

International journal of radiation oncology, biology, physics·2024
Same author

Localized high-risk prostate cancer harbors an androgen receptor low subpopulation susceptible to HER2 inhibition.

medRxiv : the preprint server for health sciences·2024
Same author

Deep learning algorithm (YOLOv7) for automated renal mass detection on contrast-enhanced MRI: a 2D and 2.5D evaluation of results.

Abdominal radiology (New York)·2024
Same author

Kidney scoring surveillance: predictive machine learning models for clear cell renal cell carcinoma growth using MRI.

Abdominal radiology (New York)·2024
Same author

Non-Invasive Tumor Grade Evaluation in Von Hippel-Lindau-Associated Clear Cell Renal Cell Carcinoma: A Magnetic Resonance Imaging-Based Study.

Journal of magnetic resonance imaging : JMRI·2024

Related Experiment Video

Updated: Apr 18, 2026

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

12.8K

Atlas based AAM and SVM model for fully automatic MRI prostate segmentation.

Ruida Cheng, Baris Turkbey, William Gandler

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 9, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new framework for automatic prostate segmentation in MR images using an active appearance model (AAM) and support vector machine (SVM) model. The proposed method achieves high accuracy, near 90%, for segmenting the prostate boundary.

    More Related Videos

    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
    Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
    04:25

    Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

    Published on: December 15, 2023

    4.2K

    Related Experiment Videos

    Last Updated: Apr 18, 2026

    Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
    09:06

    Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

    Published on: June 9, 2018

    12.8K
    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
    Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
    04:25

    Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

    Published on: December 15, 2023

    4.2K

    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Machine Learning

    Background:

    • Prostate segmentation in MR images is complex due to variations in patient anatomy and unclear boundaries.
    • Accurate segmentation is crucial for diagnosis and treatment planning.

    Purpose of the Study:

    • To develop and evaluate a supervised learning framework for accurate automatic prostate segmentation in MR images.
    • To address the challenges of inter-patient variability and unclear prostate boundaries.

    Main Methods:

    • A supervised learning framework combining an atlas-based active appearance model (AAM) and a support vector machine (SVM) model was developed.
    • The model was trained and validated using cross-validation on 40 MR image datasets.

    Main Results:

    • The combined AAM and SVM model achieved a high segmentation accuracy for the prostate boundary.
    • An average segmentation accuracy of approximately 90% was obtained.

    Conclusions:

    • The proposed framework effectively segments the prostate boundary in MR images.
    • This approach offers a promising solution for improving the accuracy and efficiency of prostate segmentation in clinical settings.