A review of recent progress in deep learning-based methods for MRI brain tumor segmentation

Abstract

Brain tumor segmentation is a challenging task that involves delimiting cancerous tissues with heterogeneous and diffuse forms in brain medical images. This process is undoubtedly an important step in computer-aided diagnosis systems, in which tumor regions must be isolated for visualization and subsequent analysis. Recently, great progress has been made in brain tumor segmentation with the emergence of deep learning-based methods, which automatically learn hierarchical, and discriminative features from raw data. These methods outperformed the classical machine learning approaches where handcrafted features are used to describe the differences between pathological and healthy tissues. In this paper, we present a comprehensive overview of recent progress in deep learning-based methods for brain tumor segmentation from magnetic resonance images. Moreover, we discuss the most common challenges and suggest possible solutions.