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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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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Machine learning and deep learning for brain tumor MRI image segmentation.

Md Kamrul Hasan Khan¹, Wenjing Guo¹, Jie Liu¹

¹National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA.

Experimental Biology and Medicine (Maywood, N.J.)

|December 16, 2023

Summary

This summary is machine-generated.

Accurate brain tumor segmentation using magnetic resonance imaging (MRI) is vital. This review covers machine learning and deep learning methods, highlighting their pros and cons for brain tumor segmentation. Combining techniques is a growing trend.

Keywords:

Machine learning brain deep learning image segmentation magnetic resonance imaging tumor

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Area of Science:

Medical Imaging
Artificial Intelligence
Neuro-oncology

Background:

Brain tumors pose significant mortality risks.
Accurate segmentation of brain tumors is crucial for patient diagnosis, treatment planning, and disease monitoring.
Magnetic resonance imaging (MRI) is a primary modality for acquiring brain images.

Purpose of the Study:

To review common machine learning (ML) and deep learning (DL) techniques used for brain tumor segmentation in MRI.
To discuss the advantages and limitations of these ML and DL methods.
To identify emerging trends in brain tumor image analysis.

Main Methods:

Review of established machine learning algorithms for image segmentation.
Review of prevalent deep learning architectures for medical image analysis.
Comparative analysis of ML and DL techniques in the context of brain tumor segmentation.

Main Results:

Both ML and DL methods have demonstrated efficacy in brain tumor MRI segmentation.
Each technique possesses distinct advantages and limitations.
The integration of multiple ML/DL techniques is an emerging and promising approach.

Conclusions:

Machine learning and deep learning are essential tools for brain tumor MRI segmentation.
Understanding the strengths and weaknesses of individual methods is key.
Hybrid approaches combining multiple techniques represent the future direction for improved segmentation accuracy and clinical utility.