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Related Experiment Video

Updated: Oct 26, 2025

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Simultaneous brain structure segmentation in magnetic resonance images using deep convolutional neural networks.

Tomoko Maruyama^1,2, Norio Hayashi³, Yusuke Sato^4,5

¹Division of Radiology, Shinshu University Hospital, 3-1-1 Asahi, Matsumoto, Nagano, 390-8621, Japan. tmaruyama@shinshu-u.ac.jp.

Radiological Physics and Technology

|August 2, 2021

Summary

This study demonstrates deep learning for automatic brain organ segmentation in MRI scans. VGG16-weighted SegNet achieved the highest precision, offering an effective approach for evaluating brain changes.

Keywords:

Brain Convolutional neural network Deep learning MRI Segmentation

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Last Updated: Oct 26, 2025

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

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Published on: January 7, 2019

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Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

Area of Science:

Medical Imaging
Artificial Intelligence
Neuroscience

Background:

Brain magnetic resonance imaging (MRI) relies on 2D T1-weighted sagittal slices for assessing brainstem atrophy and pituitary gland signals.
Accurate image segmentation is crucial for automatically tracking chronological changes in the brainstem and pituitary gland.

Purpose of the Study:

To develop and evaluate deep learning models for automatic segmentation of internal organs in brain MRI.
Specifically targeting segmentation of the brainstem, corpus callosum, pituitary, cerebrum, and cerebellum in midsagittal 2D T1-weighted images.

Main Methods:

Employed two deep learning approaches: patch-based segmentation (AlexNet, GoogLeNet, ResNet50) and semantic segmentation (SegNet, VGG16-weighted SegNet, U-Net).
Evaluated segmentation performance using precision and Jaccard index metrics across six deep convolutional neural network (DCNN) systems.

Main Results:

VGG16-weighted SegNet achieved the highest precision (0.974), while ResNet50 yielded the lowest (0.506).
Segmentation using 2D images proved to be a viable and effective method based on calculated metrics and processing times.
SegNet with VGG16 was identified as the optimal model for automatic organ segmentation in brain sagittal T1-weighted images.

Conclusions:

Deep learning, particularly VGG16-weighted SegNet, provides an effective solution for automatic segmentation of key brain structures in 2D MRI.
This automated approach facilitates accurate evaluation of anatomical changes, aiding in neurological assessments.