Synopsis of Social media discussions

The overall sentiment reveals strong support and enthusiasm for the research, exemplified by phrases like 'great new read,' and 'DL facilitates high-throughput mapping,' which emphasize the innovative and impactful nature of this work. The tone demonstrates excitement about the potential applications in large-scale brain mapping and neuroscience advancements.

A
Agreement
Moderate agreement

Most discussions acknowledge the significance of using CNNs for brain mapping, showing general support for the methodology and findings.

I
Interest
High level of interest

Posts are highly interested, referencing the innovative approach and its potential impact on neuroscience research.

E
Engagement
Moderate level of engagement

Some discussions delve into technical details and implications, indicating active engagement.

I
Impact
High level of impact

Multiple comments highlight the transformative potential of this technology for brain research and high-throughput mapping.

Social Mentions

YouTube

2 Videos

Twitter

16 Posts

Metrics

Video Views

1,304

Total Likes

31

Extended Reach

81,564

Social Features

18

Timeline: Posts about article

Top Social Media Posts

Posts referencing the article

Automated 3D Cytoarchitectonic Mapping of the Human Metathalamus

Automated 3D Cytoarchitectonic Mapping of the Human Metathalamus

This video discusses an innovative workflow utilizing deep convolutional neural networks to enhance the speed and accuracy of cytoarchitectonic brain mapping, facilitating large-scale analysis of human brain sections without requiring 3D reconstruction.

February 14, 2022

1,094 views


Automated Large-Scale Brain Mapping with Convolutional Neural Networks

Automated Large-Scale Brain Mapping with Convolutional Neural Networks

This study introduces a new automated workflow using Deep Convolutional Neural Networks to map cytoarchitectonic areas in large human brain series, improving speed and accuracy, and enabling high-resolution brain mapping for better understanding of brain organization.

September 13, 2021

210 views


  • Kai Kiwitz
    @KaiKiwitz (Twitter)

    see @brandst_and and me talk about our new subcortical brain maps @EBRAINS_eu https://t.co/lUs5rtk9wE
    view full post

    May 12, 2022

    2

  • The Aegean Society
    @aegeansociety (Twitter)

    Araştırmacılar, metatalamusun iki farklı bölümünün hücresel yapısının yeni bir açık erişimli 3D haritasını yayınladılar. #humanbrainproject #insanbeyinprojesi https://t.co/7BPbiscBn3
    view full post

    April 2, 2022

  • UCL Discovery
    @ucl_discovery (Twitter)

    Open Access UCL Research: Convolutional neural networks for cytoarchitectonic brain mapping at large scale https://t.co/XtOiKVV9U5
    view full post

    August 5, 2021

  • BigBrain
    @BigBrainProject (Twitter)

    RT @bogglerapture: Convolutional neural networks for cytoarchitectonic brain mapping at large scale https://t.co/e5vizd0jV1
    view full post

    July 12, 2021

    4

  • Tristan Chaplin
    @chaplin_ta (Twitter)

    RT @bogglerapture: Convolutional neural networks for cytoarchitectonic brain mapping at large scale https://t.co/e5vizd0jV1
    view full post

    July 11, 2021

    4

  • Alex Fornito
    @AFornito (Twitter)

    RT @bogglerapture: Convolutional neural networks for cytoarchitectonic brain mapping at large scale https://t.co/e5vizd0jV1
    view full post

    July 11, 2021

    4

  • Walid Yassin
    @Wal_yas (Twitter)

    RT @bogglerapture: Convolutional neural networks for cytoarchitectonic brain mapping at large scale https://t.co/e5vizd0jV1
    view full post

    July 10, 2021

    4

  • Armin Raznahan
    @bogglerapture (Twitter)

    Convolutional neural networks for cytoarchitectonic brain mapping at large scale https://t.co/e5vizd0jV1
    view full post

    July 10, 2021

    12

    4

  • Helmholtz AI
    @helmholtz_ai (Twitter)

    RT @wenzel_susanne: Convolutional Neural Networks for cytoarchitectonic brain mapping at large scale. Great new read at #neuroimage
    view full post

    July 5, 2021

    1

  • MRI Papers
    @mripapers (Twitter)

    NI: Convolutional Neural Networks for cytoarchitectonic brain mapping at large scale https://t.co/4lI7Z31tOL
    view full post

    July 3, 2021

  • Christian Schiffer
    @ChristianSchif8 (Twitter)

    RT @BigBrainProject: DL facilitates high-throughput cytoarchitectonic mapping in large series of histological brain sections
    view full post

    July 2, 2021

    1

  • BigBrain
    @BigBrainProject (Twitter)

    DL facilitates high-throughput cytoarchitectonic mapping in large series of histological brain sections
    view full post

    July 2, 2021

    5

    1

  • Deep RL
    @deep_rl (Twitter)

    Convolutional Neural Networks for cytoarchitectonic brain mapping at large scale - Christian Schiffer https://t.co/Npxi9o6YAZ
    view full post

    November 27, 2020

  • DeepAI
    @DeepAI (Twitter)

    Convolutional Neural Networks for cytoarchitectonic brain mapping at large scale https://t.co/HW31DoT9TQ by Christian Schiffer et al. #NeuralNetwork #ConvolutionalNeuralNetwork
    view full post

    November 27, 2020

    1

  • cs.CV Papers
    @arxiv_cs_cv_pr (Twitter)

    Convolutional Neural Networks for cytoarchitectonic brain mapping at large scale. Christian Schiffer, Hannah Spitzer, Kai Kiwitz, Nina Unger, Konrad Wagstyl, Alan C. Evans, Stefan Harmeling, Katrin Amunts, and Timo Dickscheid https://t.co/ep9y9WrqJi
    view full post

    November 26, 2020

  • arXiv reaDer bot (cs-CV)
    @arXiv_reaDer (Twitter)

    Convolutional Neural Networks for cytoarchitectonic brain mapping at large scale 大規模な細胞構築脳マッピングのための畳み込みニューラルネットワーク 2020-11-25T16:25:13+00:00 arXiv: https://t.co/ZlbkHazPbf 英/日サマリ↓ https://t.co/z5hBmUMfcr
    view full post

    November 25, 2020

Abstract Synopsis

  • This study introduces a new automated workflow using Deep Convolutional Neural Networks (CNNs) to map cytoarchitectonic areas in large series of human brain sections, improving speed and accuracy compared to previous methods.
  • The method learns from a few annotated section images and predicts missing annotations across unannotated sections, eliminating the need for 3D reconstruction and handling artifacts effectively, making it suitable for large datasets.
  • The workflow is accessible through a web interface, enabling researchers without deep learning expertise to analyze massive brain data, paving the way for high-resolution brain mapping and better understanding of brain organization.]