Evolution of Deep Learning Algorithms for MRI-Based Brain Tumor Image Segmentation.

Abstract

Brain tumor textures are among the most challenging features for neuroradiologists to extract from magnetic resonance images (MRIs). Exceptionally high-grade tumors such as gliomas require quick and precise diagnosis and medical intervention due to their infiltrative and fast-spreading nature. Therefore, they require computer assistance instead of manual methods. Deep learning (DL) methods are currently on the rise and have become an active field of research in several domains varying from stock market analysis to deep space object detection. They have very promising potential in brain tumor feature extraction from MRIs. Convolutional neural network (CNN) architectures, one of the most influential families of DL algorithms, have undergone a profound transformation since their first successes. This has led to increasing feature extraction quality and algorithm generalizability over various brain tumor types and grades. This review paper presents an explanatory and comparative survey on MRI-based brain tumor image segmentation. First, it provides the survey background and the typical process chain for brain MRI segmentation using CNNs. Second, it details the typical CNN architecture structure and its advantages over other machine learning algorithms. CNN architectures proposed for this purpose are enumerated and classified corresponding to their complexity, and then compared using specific metrics that consider the datasets they use.