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 Experiment Video

Updated: Jun 28, 2026

Micro-CT Imaging and Morphometric Analysis of Mouse Neonatal Brains
06:36

Micro-CT Imaging and Morphometric Analysis of Mouse Neonatal Brains

Published on: May 19, 2023

CytoCLIP: Learning Cytoarchitectural Characteristics in Developing Human Brain Using Contrastive Language Image

Pralaypati Ta1,2, Sriram Venkatesaperumal3, Keerthi Ram3

  • 1Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, India. pralaypati@htic.iitm.ac.in.

Neuroinformatics
|June 26, 2026
PubMed
Summary

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

AD-DAE: Alzheimer's Disease Progression Modeling with Unpaired Longitudinal MRI using Diffusion Auto-Encoders.

IEEE journal of biomedical and health informatics·2026
Same author

Unobtrusive Heart Rate Variability Monitoring with a Chair-Based Strain Gauge Ballistocardiography in Office and Home Settings.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

MPM-DCE: Multi-stage Progressive Mechanism for Early and Late DCE Prostate MRI Synthesis.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Image-free system for non-contact cardiopulmonary monitoring in supine posture.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Boosting CCA Pulse Accuracy: How ICA Enhances Reliability in Wearable Accelerometer Devices.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Vision Transformer and NeoPulseNet: A Dual Approach for Accurate rPPG Signal Extraction in Neonates.

IEEE journal of biomedical and health informatics·2025
Same journal

Increasing the Reliability of Functional Connectivity by Predicting Long-Scan Functional Connectivity based on Short-Scan Functional Connectivity: Model Exploration, Explanation, Validation, and Application.

Neuroinformatics·2026
Same journal

HESREN: A Derivative-Informed Reservoir Framework for Detecting Transient Neural Events and Windowless Estimation of Dynamic Functional Connectivity.

Neuroinformatics·2026
Same journal

Computational Morphometry of Peripheral Nerves: A Pipeline Perspective on Reproducibility and Generalization.

Neuroinformatics·2026
Same journal

Multimodal Branched Transport Infers Anatomically Aligned Brain Reaction Maps.

Neuroinformatics·2026
Same journal

Model Validation Pipeline Against Longitudinal Alzheimer's Biomarker Data.

Neuroinformatics·2026
Same journal

When Random Variation Results in Functional Segregation.

Neuroinformatics·2026
See all related articles
This summary is machine-generated.

CytoCLIP, a novel AI tool, automates brain region identification using cytoarchitecture. This vision-language model accurately classifies brain areas from histological images, aiding neuroscience research.

Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Computational Biology

Background:

  • Brain region function is linked to cytoarchitecture (cell arrangement and morphology).
  • Manual identification of brain regions in histological sections is labor-intensive and requires expertise.
  • Automated methods are needed to streamline brain region analysis.

Purpose of the Study:

  • To develop an automated approach for identifying brain regions based on cytoarchitecture.
  • To introduce CytoCLIP, a suite of vision-language models for learning joint visual-text representations of brain cytoarchitecture.
  • To improve the efficiency and accuracy of brain region delineation in histological analysis.

Main Methods:

  • Utilized pre-trained Contrastive Language-Image Pre-Training (CLIP) frameworks to create CytoCLIP.
Keywords:
CLIPContrastive learningCytoarchitectureHistological Image processing

More Related Videos

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy
08:49

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Published on: August 1, 2022

Biocytin Recovery and 3D Reconstructions of Filled Hippocampal CA2 Interneurons
11:21

Biocytin Recovery and 3D Reconstructions of Filled Hippocampal CA2 Interneurons

Published on: November 20, 2018

Related Experiment Videos

Last Updated: Jun 28, 2026

Micro-CT Imaging and Morphometric Analysis of Mouse Neonatal Brains
06:36

Micro-CT Imaging and Morphometric Analysis of Mouse Neonatal Brains

Published on: May 19, 2023

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy
08:49

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Published on: August 1, 2022

Biocytin Recovery and 3D Reconstructions of Filled Hippocampal CA2 Interneurons
11:21

Biocytin Recovery and 3D Reconstructions of Filled Hippocampal CA2 Interneurons

Published on: November 20, 2018

  • Developed two model variants: one for low-resolution whole-region analysis and another for high-resolution cellular-level analysis.
  • Trained models on NISSL-stained fetal brain histological sections, covering 86 regions (low-res) and 379 regions (high-res).

    • Main Results:

    • CytoCLIP achieved a weighted F1 score of 0.87 for whole-region classification.
    • The high-resolution model variant achieved a weighted F1 score of 0.91 for image tile classification.
    • Demonstrated superior performance compared to existing methods in region classification and cross-modal retrieval tasks.

    Conclusions:

    • CytoCLIP effectively automates the identification of brain regions by cytoarchitecture.
    • The developed models show strong understanding and generalization capabilities for brain cytoarchitectural patterns.
    • CytoCLIP offers a promising solution for accelerating neuroscience research through efficient histological analysis.