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

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Image Segmentation for MR Brain Tumor Detection Using Machine Learning: A Review.

Toufique A Soomro, Lihong Zheng, Ahmed J Afifi

IEEE Reviews in Biomedical Engineering

|June 23, 2022

Summary

This summary is machine-generated.

This review examines machine learning and image segmentation for brain tumor identification using Magnetic Resonance Imaging (MRI). Deep learning methods show superior effectiveness in segmenting brain tumors from MRI scans.

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Area of Science:

Medical Imaging
Artificial Intelligence
Neurology

Background:

Magnetic Resonance Imaging (MRI) is a crucial non-invasive tool for diagnosing brain diseases and monitoring treatment.
Manual analysis of MRI scans for brain anomalies is time-consuming and labor-intensive.
Automated analysis using machine learning offers a faster and more accurate approach to identifying abnormalities.

Approach:

This article reviews research papers from 1998 to 2020 focusing on brain tumor segmentation from MRI images.
Core segmentation algorithms from selected studies are examined in detail.
Various machine learning and image segmentation techniques applied to brain tumor identification are explored.

Key Points:

Image segmentation is a critical area in medical image analysis for computer-aided diagnosis.
Machine learning algorithms enhance the speed and accuracy of identifying brain abnormalities.
Deep learning methods have emerged as highly effective for segmenting brain tumors in MRI scans.

Conclusions:

The review provides a comprehensive overview of brain tumor segmentation techniques using MRI.
It highlights the evolution and application of machine learning in medical image analysis.
Deep learning demonstrates superior performance for brain tumor segmentation compared to traditional methods.