Automatic segmentation of the thalamus using a massively trained 3D convolutional neural network: higher sensitivity for the detection of reduced thalamus volume by improved inter-scanner stability.

Roland Opfer, Julia Krüger, Lothar Spies, Ann-Christin Ostwaldt, Hagen H Kitzler, Sven Schippling, Ralph Buchert

March 2023 Eur Radiol

Synopsis of Social media discussions

Many discussions express strong interest by highlighting the method's advancements, such as the use of a 3D convolutional neural network for accurate and stable thalamus segmentation across different MRI scanners. The tone reflects enthusiasm about the research’s potential to enhance diagnosis, demonstrated by phrases like 'higher sensitivity' and 'improved inter-scanner stability,' indicating both relevance and visionary potential.

A
Agreement
 Moderate agreement

Most discussions acknowledge the significance of the research, especially with mentions of improved detection methods and clinical relevance.

I
Interest
 High level of interest

People show high interest, referencing both technical aspects and potential applications in neuroimaging and disease monitoring.

E
Engagement
 Moderate level of engagement

Participants actively engage with the material, discussing datasets used and potential implications, though some focus remains on the methodology rather than deep critique.

I
Impact
 High level of impact

The conversations suggest this research could have substantial influence on neuroimaging practices and clinical diagnostics, highlighted by terms like 'higher sensitivity' and 'improved stability'.

Social Mentions

YouTube

2 Videos

Twitter

3 Posts

Metrics

Video Views

1,055

Total Likes

22

Extended Reach

1,778

Social Features

5

Timeline: Posts about article

Top Social Media Posts

Posts referencing the article

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Automatic Thalamus Segmentation Using 3D CNN for MRI Analysis

This video explains the development of an automatic thalamus segmentation method using a 3D convolutional neural network on diverse T1-weighted MRI data, achieving high accuracy and improved stability across scanners for better clinical diagnosis and monitoring.

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November 6, 2022

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Thalamus Segmentation with Deep Learning for Improved MRI Analysis

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This video explains the development of an automatic thalamus segmentation method using a 3D convolutional neural network on diverse MRI data, achieving high accuracy and stability across scanners. The approach enhances detection of thalamus atrophy, aiding clinical diagnosis and monitoring.

BrAIn MRI

November 17, 2022

491 views

  • Max Korbmacher
    @KorbmacherMax (Twitter)

    We used T1-weighted data from two densly sampled MRI datasets (few subjects, many data points) to look at #BrainAge: a) the Bergen Breakfast Scanning Club https://t.co/VybxibQKz6 b) the Frequently Traveling Human Phantom https://t.co/8FZKmHNdbn
    view full post

    April 3, 2023

  • omu_radiology_bot
    @omuradiologybot (Twitter)

    Automatic segmentation of the thalamus using a massively trained 3D convolutional neural network: higher sensitivity for the detection of reduced thalamus volume by improved inter-scanner stability. #EurRadiol https://t.co/qwqSJaWg0M
    view full post

    October 21, 2022

  • Paperbirds_Neurology
    @PaperbirdsN (Twitter)

    New article: Automatic segmentation of the thalamus using a massively trained 3D convolutional neural network: higher sensitivity for the detection of reduced thalamus volume by improved inter-scanner stability https://t.co/A6N4bgWjkW #MS #multiplesclerosis #neurology https://t.co/yqm00evmnN
    view full post

    October 20, 2022

Abstract Synopsis

  • Developed an automatic thalamus segmentation method using a 3D convolutional neural network (CNN) on diverse T1-weighted MRI data, eliminating the need for standard scanner protocols.
  • Achieved high accuracy in segmenting the thalamus, comparable to existing methods, with better stability in volume estimates across different MRI scanners.
  • The 3D CNN demonstrated superior sensitivity in detecting thalamus atrophy in multiple sclerosis patients, indicating potential for improved clinical diagnosis and monitoring.