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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Related Experiment Video

Updated: Jul 16, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

A fast and accurate brain extraction method for CT head images.

Dingyuan Hu¹, Hongbin Liang², Shiya Qu¹

¹School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, NO.185 in Qianshan Middle Street, Anshan, 114000, Liaoning Province, PR China.

BMC Medical Imaging

|September 12, 2023

Summary

This study introduces FABEM, a fast and accurate algorithm for brain extraction from head CT scans. It improves upon existing methods by significantly reducing processing time while maintaining high diagnostic accuracy for intracranial lesions.

Keywords:

Brain extraction Fully convolutional neural network Head CT images Threshold segmentation

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Last Updated: Jul 16, 2025

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Published on: April 14, 2014

Area of Science:

Medical Imaging
Artificial Intelligence in Medicine
Neuroscience

Background:

Accurate brain extraction is crucial for diagnosing intracranial lesions.
Fully convolutional neural networks (FCNs) offer high accuracy but slow extraction speeds.

Purpose of the Study:

To develop an integrated algorithm (FABEM) for faster and more accurate brain extraction from head CT images.
To address the speed limitations of current FCN-based segmentation methods.

Main Methods:

FABEM utilizes threshold segmentation, closed operations, CNNs, and image filling to create a mask.
The algorithm adapts segmentation based on the number of connected regions in the mask, using region growing or DeepLabv3+ for refinement.
The final brain mask is multiplied with the original image for extraction.

Main Results:

FABEM achieved comparable performance metrics (MPA=0.9968, MIoU=0.9936, MBF=0.9963) to DeepLabv3+.
FABEM demonstrated significantly faster extraction speeds, completing head CT brain extraction in approximately 0.43 seconds, 3.8 times quicker than DeepLabv3+.

Conclusions:

The FABEM method enables rapid and precise brain extraction from head CT images.
This facilitates improved brain volume measurement and feature extraction for intracranial lesions.