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

Scaling Multimodal Agentic AI in Medical Education: Multisite Cross-Sectional Study of Simulation Effectiveness in Primary Care.

JMIR formative research·2026
Same author

Beyond Weight Loss: Holistic Impacts of a Digital Weight Management Programme Integrating Tirzepatide.

Cureus·2026
Same author

Digital Engagement Significantly Enhances Weight Loss Outcomes in Adults With Obesity Treated With Tirzepatide: Retrospective Cohort Study of a Digital Weight Loss Service.

Journal of medical Internet research·2026
Same author

Real-World Outcomes and Safety of Testosterone Therapy: A Longitudinal, Retrospective Cohort Study of Over 9,000 Men.

The world journal of men's health·2026
Same author

Digital engagement enhances dual GIP/GLP-1 receptor agonist and GLP-1 receptor agonist efficacy: A retrospective cohort analysis of a digital weight loss service on outcomes and safety.

Diabetes, obesity & metabolism·2025
Same author

Application of AI Communication Training Tools in Medical Undergraduate Education: Mixed Methods Feasibility Study Within a Primary Care Context.

JMIR medical education·2025
Same journal

AVA: Automated Viewability Analysis for Ureteroscopic Intrarenal Surgery.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Kidney Endoscopy Video to Preoperative CT Alignment for Depth Estimation.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Deep learning‑based cell type prediction in lung tissue from brightfield histology using CODEX-derived labels.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Reconstructing physiological signals from fMRI across the adult lifespan.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Axially Swept Light-Sheet Microscopy using scattering and fluorescence contrast mechanisms.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same journal

Analytic Bounds on GAMLSS Model Variability of Normative White Matter Brain Charts.

Proceedings of SPIE--the International Society for Optical Engineering·2026
See all related articles

Related Experiment Video

Updated: Oct 11, 2025

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

3.0K

Longitudinal subcortical segmentation with deep learning.

Hao Li1, Huahong Zhang1, Hans Johnson2

  • 1Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235.

Proceedings of Spie--The International Society for Optical Engineering
|December 7, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new deep learning method for analyzing brain changes in Huntington's disease (HD) using longitudinal MRI scans. The approach improves the accuracy of tracking subcortical atrophy over time, enhancing disease progression monitoring.

Keywords:
Bi-directional C-LSTMDeep LearningHuntington’s DiseaseLongitudinalMRISubcortical Segmentation

More Related Videos

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

9.6K
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

9.1K

Related Experiment Videos

Last Updated: Oct 11, 2025

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

3.0K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

9.6K
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

9.1K

Area of Science:

  • Neuroimaging
  • Neurodegenerative Diseases
  • Artificial Intelligence in Medicine

Background:

  • Longitudinal data is crucial for monitoring neurodegenerative diseases like Huntington's disease (HD).
  • Subcortical atrophy (caudate, putamen) is a key MRI marker for HD progression.
  • Current deep learning segmentation methods often neglect longitudinal data structure.

Purpose of the Study:

  • To develop a deep learning method for subcortical segmentation that utilizes longitudinal MRI information.
  • To improve the accuracy and consistency of tracking brain changes in HD patients over time.
  • To enhance the correlation between imaging markers and clinical outcomes in HD.

Main Methods:

  • Proposed a novel deep learning model for joint segmentation of longitudinal 3D MRI pairs.
  • Incorporated bi-directional convolutional long short-term memory (C-LSTM) blocks to leverage temporal information.
  • Evaluated the method on the PREDICT-HD dataset using Dice, ASD, and 95% Hausdorff distance metrics.

Main Results:

  • The proposed longitudinal method demonstrated improved segmentation accuracy compared to cross-sectional approaches.
  • Achieved more consistent segmentation performance across multiple time points for individual subjects.
  • Identified a stronger correlation between subcortical volume loss and motor decline in HD patients.

Conclusions:

  • Deep learning models incorporating longitudinal MRI data can enhance the monitoring of neurodegenerative disease progression.
  • This method offers a more accurate and consistent way to measure subcortical atrophy in Huntington's disease.
  • Improved segmentation facilitates better understanding of the relationship between brain changes and clinical symptoms in HD.