# Conventional and Deep Learning Methods for Skull Stripping in Brain MRI
###### 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. skull stripping; brain segmentation; brain extraction; deep convolutional neural networks; U-Net +
Footnote †: journal: Medical Imaging  
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(PDW), and T\({}_{2}\)W with fluid-attenuated inversion recovery (FLAIR). However, these modalities do not have standardized acquisition parameters [7]. T\({}_{1}\)W is the most extensively used sequence for the brain and other cranial structural analysis, as it provides an easy annotation of the healthy tissues. Brain segmentation, also recognized as brain extraction or skull stripping, is a pipeline of segmenting (generally) a T\({}_{1}\)W brain MRI volume into brain and non-brain regions [8], as portrayed in Figure 1. This is an initial and important preprocessing step of most brain MRI studies, such as brain tissue segmentation and volumetric measurement [9; 10], longitudinal analysis [11], multiple sclerosis analysis [12], and cortical and sub-cortical analysis [13]; assessing schizophrenia [14]; and for planning of neurosurgical interventions [15]. Accurate skull stripping is a critical step for a neuroimaging diagnostic system because neither the inclusion of non-brain tissues (skull, dura mater, etc.) nor removal of brain parts (under segmentation) can be rectified in subsequent steps, which can lead to the propagation of error through succeeding analysis. For instance, skull stripping is the first stage in cortical reconstruction and brain volume measurement [16]. The inclusion of non-brain tissue in brain region can leads to incorrect reconstruction of cortical surface and brain volume computation in the later stages. [PERSON] et al. [17] demonstrated that the complete initial skull stripping from brain MRI results in a more accurate and sensitive analysis of voxel-based morphometry (VBM) in brain morphology. [PERSON] et al. [18] has also investigated the impact of skull stripping on grey-matter segmentation for VBM and proved that this preprocessing step has a major impact on the VBM results. After the detailed structural and functional investigation, [PERSON] et al. [19] revealed that skull stripping is the most important factor to improving the congruence between the Montreal Neurological Institute (MNI) template and normalized brains. Furthermore, many brain related imaging applications require (or benefit from) the ability to precisely segment the brain from the skull. For example, registration robustness can be improved if the non-brain tissues are automatically eliminated before registration of brain MRIs [17; 18]. In another brain atrophy estimation example in diseased subjects, brain volume is calculated with respect to some normalizing volume (e.g., skull or head size) at a single time point after skull stripping. In other words, brain images from two or more-time intervals from the same subject are compared to assess how the brain has altered over time [20; 21].