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Updated: Oct 5, 2025

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

Published on: July 2, 2014

Learning white matter subject-specific segmentation from structural MRI.

Qi Yang¹, Colin B Hansen¹, Leon Y Cai²

¹Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA.

Medical Physics

|January 28, 2022

Summary

This summary is machine-generated.

Deep learning can now generate white matter (WM) tracts from structural MRI, overcoming limitations of time-intensive diffusion MRI (dMRI) scans. This method offers accurate WM pathway visualization when dMRI is unavailable.

Keywords:

T1 weight MRI learning methods and patch-wise deep neural network tractography algorithms white matter

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Area of Science:

Neuroimaging
Computational Neuroscience
Artificial Intelligence

Background:

Brain white matter (WM) mapping is crucial for understanding brain anatomy and function.
Diffusion-weighted MRI (dMRI) tractography is the primary method for WM investigation.
dMRI acquisitions are time-consuming and not always feasible for all studies.

Purpose of the Study:

To develop a deep learning framework for generating WM tracts from structural MRI.
To address the limitations of dMRI availability in tractography.

Main Methods:

Utilized multiply spatial localized convolution neural networks (U-Net models) trained on structural MRI.
Trained 125 U-Net models on 3870 T1-weighted images from multiple datasets.
Focused on learning six established dMRI tractography algorithms.

Main Results:

The framework achieved high agreement with dMRI tractography, with median Dice coefficients ranging from 0.62 to 0.87.
Demonstrated improved subject-specific accuracy compared to population atlas-based methods.
Validated the generalizability of the framework on three external datasets.

Conclusions:

Patch-wise convolutional neural networks can robustly segment WM bundles from T1-weighted MRI.
The framework enables WM pathway visualization in scenarios where dMRI is not accessible.