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Related Experiment Video

Updated: Dec 25, 2025

Rigid Embedding of Fixed and Stained, Whole, Millimeter-Scale Specimens for Section-free 3D Histology by Micro-Computed Tomography

Rigid Embedding of Fixed and Stained, Whole, Millimeter-Scale Specimens for Section-free 3D Histology by Micro-Computed Tomography

Published on: October 17, 2018

LEARNING 3D WHITE MATTER MICROSTRUCTURE FROM 2D HISTOLOGY.

Vishwesh Nath¹, Kurt G Schilling², Samuel Remedios¹

¹Department of Computer Science, Vanderbilt University, Nashville, TN.

Proceedings. IEEE International Symposium on Biomedical Imaging

|March 27, 2020

Summary

This summary is machine-generated.

Researchers developed a novel method using convolutional neural networks to reconstruct 3D brain tissue microstructure from 2D histology images. This technique accurately estimates fiber orientation distributions, advancing diffusion MRI validation and enabling high-resolution tractography.

Keywords:

connectivity convolution neural network diffusion MRI histology microstructure validation

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3D Modeling of the Lateral Ventricles and Histological Characterization of Periventricular Tissue in Humans and Mouse

3D Modeling of the Lateral Ventricles and Histological Characterization of Periventricular Tissue in Humans and Mouse

Published on: May 19, 2015

Area of Science:

Neuroimaging
Computational Neuroscience
Histology

Background:

Histological analysis is the gold standard for validating magnetic resonance imaging (MRI) measures of tissue microstructure.
Most histological analyses are 2D, limiting their direct application for validating 3D MRI data.
Reconstructing 3D microstructure from 2D histology is of significant interest.

Purpose of the Study:

To develop a method for learning 3D tissue microstructure from 2D histology.
To establish the relationship between 3D diffusion MRI (dMRI) fiber orientation distributions and 2D myelin stains.
To enable validation of dMRI measurements using limited 2D histology data.

Main Methods:

Utilized diffusion MRI (dMRI) data from a squirrel monkey brain.
Employed 2D myelin-stained histological sections.
Applied a convolutional neural network (CNN) to learn the mapping between dMRI and histology data.

Main Results:

The CNN successfully estimated 3D fiber orientation distributions from 2D myelin stains.
Achieved moderate to high angular agreement with ground truth (median angular correlation coefficient of 0.48).
Demonstrated potential for generalization to human stained sections.

Conclusions:

The developed network can validate 3D dMRI neuronal structural measurements using 2D histology.
Enables inference of 3D fiber distributions at resolutions beyond current dMRI capabilities.
Paves the way for high-resolution diffusion fiber tractography and learning other 3D microstructural measures from 2D histology.