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Overview of Multi-Modal Brain Tumor MR Image Segmentation
Wenyin Zhang1, Yong Wu1, Bo Yang2
1School of Information Science and Engineering, Linyi University, Linyi 276000, China.
Healthcare (Basel, Switzerland)
|August 27, 2021
Summary
This survey reviews brain tumor segmentation using Magnetic Resonance Imaging (MRI). It categorizes methods, analyzes challenges, and forecasts future trends in brain tumor image analysis.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Radiology
Background:
- Accurate brain tumor segmentation is crucial for diagnosis and treatment planning.
- Magnetic Resonance Imaging (MRI) offers detailed, non-invasive visualization of brain structures.
- Existing segmentation techniques vary in complexity and efficacy.
Purpose of the Study:
- To provide a comprehensive overview of brain tumor MRI image segmentation.
- To categorize and analyze different segmentation methodologies.
- To identify current challenges and future research directions.
Main Methods:
- The study surveys commonly used brain tumor MRI databases.
- It categorizes segmentation methods into conventional, classical machine learning, and deep learning approaches.
- Key algorithms within each category are analyzed for their principles, structures, advantages, and disadvantages.
Main Results:
- A structured overview of segmentation techniques for brain tumor MRI is presented.
- Comparative analysis of conventional, classical machine learning, and deep learning methods is provided.
- Identified limitations and potential improvements in current segmentation strategies.
Conclusions:
- Segmentation of brain tumor MRI images is a rapidly evolving field.
- Deep learning methods show significant promise but require further refinement.
- Future research should focus on addressing current challenges for enhanced clinical application.
