Advancements of MRI-based Brain Tumor Segmentation from Traditional to Recent Trends: A Review.

Abstract

Among brain-related diseases, brain tumor segmentation on magnetic resonance imaging (MRI) scans is one of the highly focused research domains in the medical community. Brain tumor segmentation is challenging due to its asymmetric form and uncertain boundaries. This process segregates the tumor region into the active tumor, necrosis, and edema from normal brain tissues such as white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF). The proposed paper analyzed the advancement of brain tumor segmentation from conventional image processing techniques to deep learning through machine learning on MRI of human head scans. State-of-the-art methods of these three techniques are investigated, and the merits and demerits are discussed. The primary aim of the paper is to motivate young researchers towards the development of efficient brain tumor segmentation techniques using conventional as well as recent technologies. The proposed analysis concluded that the conventional and machine learning methods were mainly applied for brain tumor detection, whereas deep learning methods were good at segmenting tumor substructures.