Conventional and Deep Learning Methods for Skull Stripping in Brain MRI

Abstract

Featured Application Skull stripping is the most prevalent brain image analysis method. This method can be applied to areas such as brain tissue segmentation and volumetric measurement, longitudinal analysis, multiple sclerosis analysis, cortical and sub-cortical analysis, assessing schizophrenia, and for the planning of neurosurgical interventions. Abstract Skull stripping in brain magnetic resonance volume has recently been attracting attention due to an increased demand to develop an efficient, accurate, and general algorithm for diverse datasets of the brain. Accurate skull stripping is a critical step for neuroimaging diagnostic systems because neither the inclusion of non-brain tissues nor removal of brain parts can be corrected in subsequent steps, which results in unfixed error through subsequent analysis. The objective of this review article is to give a comprehensive overview of skull stripping approaches, including recent deep learning-based approaches. In this paper, the current methods of skull stripping have been divided into two distinct groups-conventional or classical approaches, and convolutional neural networks or deep learning approaches. The potentials of several methods are emphasized because they can be applied to standard clinical imaging protocols. Finally, current trends and future developments are addressed giving special attention to recent deep learning algorithms.