Data Augmentation for Brain-Tumor Segmentation: A Review.

Area of Science:

• Medical Image Analysis
• Deep Learning
• Artificial Intelligence

Background:

• Limited high-quality medical data hinders deep neural network generalization.
• Acquiring medical imaging datasets, particularly for brain tumor delineation, is often costly and time-consuming.
• Data augmentation serves as a crucial implicit regularization technique in such scenarios.

Purpose of the Study:

• To review current data augmentation techniques for brain tumor magnetic resonance imaging (MRI).
• To investigate the practical application and impact of data augmentation in the BraTS 2018 challenge.
• To identify future research directions for synthesizing high-quality artificial brain tumor data.

Main Methods:

• Literature review of data augmentation techniques in brain tumor MRI.
• Analysis of papers from the Multimodal Brain Tumor Segmentation Challenge (BraTS 2018).
• Evaluation of the impact of various augmentation approaches on supervised learning models.

Main Results:

• Identified commonly exploited data augmentation methods in the BraTS 2018 challenge.
• Assessed the influence of these augmentation strategies on the performance of deep learning models for brain tumor segmentation.
• Highlighted the effectiveness of data augmentation in improving model generalization.

Conclusions:

• Data augmentation is vital for enhancing deep learning models in medical image analysis, especially for brain tumor segmentation.
• The BraTS dataset serves as a key benchmark for evaluating augmentation strategies.
• Further research into synthesizing realistic artificial brain tumor data is essential for advancing deep learning applications.