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A Review on Deep Learning Architecture and Methods for MRI Brain Tumour Segmentation
1School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India.
Current Medical Imaging
|January 11, 2021
Summary
Deep learning methods offer state-of-the-art performance for brain tumour segmentation in MRI images, eliminating the need for handcrafted features. This review explores deep learning architectures and methods for this crucial medical imaging task.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Neuroscience
Background:
- Automatic segmentation of brain tumours from MRI is critical for diagnosis and treatment planning.
- Deep learning approaches have recently achieved state-of-the-art performance in medical image analysis tasks, including segmentation.
Purpose of the Study:
- To review deep learning architectures and methods specifically for MRI brain tumour segmentation.
- To analyze the advantages and disadvantages of various deep learning techniques in this domain.
Main Methods:
- The review covers fundamental deep learning architectures and approaches.
- A literature survey of deep learning methods for MRI brain tumour segmentation, including multimodality fusion, is presented.
Main Results:
- Deep learning techniques provide hierarchical feature representation, bypassing the need for handcrafted features.
- The review offers insights into brain tumour identification using deep learning.
Conclusions:
- Deep learning techniques present significant merits for MRI brain tumour segmentation.
- Further research focus on the challenges and advancements in deep learning for this application is recommended.
