MRI segmentation of the human brain: challenges, methods, and applications.

Area of Science:

• Medical Image Analysis
• Neuroimaging
• Computational Anatomy

Background:

• Image segmentation is crucial in medical imaging, particularly for brain MRI analysis.
• Applications include anatomical measurement, change analysis, pathology delineation, and surgical planning.
• Numerous segmentation techniques with varying complexity and accuracy exist.

Purpose of the Study:

• To review popular brain MRI segmentation methods.
• To highlight differences, capabilities, advantages, and limitations of these techniques.
• To discuss preprocessing steps and validation challenges in brain MRI segmentation.

Main Methods:

• Introduction to fundamental image segmentation concepts.
• Explanation of MRI preprocessing: registration, bias field correction, non-brain tissue removal.
• Review and comparison of common brain MRI segmentation algorithms.

Main Results:

• The paper provides an overview of established brain MRI segmentation techniques.
• It contrasts various methods, detailing their strengths and weaknesses.
• Key preprocessing steps and validation strategies are presented.

Conclusions:

• Understanding different segmentation methods is vital for accurate brain MRI analysis.
• Appropriate preprocessing and validation are essential for reliable results.
• This review serves as a guide to selecting and applying brain MRI segmentation techniques.