# Conventional and Deep Learning Methods for Skull Stripping in Brain MRI
###### Abstract
Manual skull stripping is a laborious task due to low contrast images, obscure boundaries of the brain in the MRI, and the absence of intensity standardization [22]. Moreover, the whole brain extraction becomes more challenging and limited in the presence of varying acquisition parameters or when a brain MRI dataset presents a pathological disorder, such as a brain tumor [23]. Figure 1: Skull stripping in brain MRIs. (**a**) Input 2D brain image in three orientations (coronal, axial, and sagittal), and (**b**) respective skull stripping in each image. (**c**,**d**) Similarly, the skull stripping in brain MRI volume (data from NFBS dataset [24]). For skull stripping in brain MRIs, manual brain and non-brain segmentation methods are considered more robust and accurate than semi or fully automatic methods. The aim of manual skull stripping is to draw the boundaries on brain MRIs and isolate the brain tissue from the skull (or other non-brain regions). Generally, manual delineation of the brain tissue is treated as the \"ground truth\" or \"gold standard\" and is commonly utilized to validate other semi-automatic and automatic skull stripping methods. However, there are several reasons which prevent it from being a feasible solution in most biomedical and neuroimaging applications. Primarily, it is a laborious, error-prone, and time-consuming process. For instance, manual delineation of the brain takes approximately 15 min to 2 h for a single 3-dimensional (3D) volume of brain MRI [25]. It requires urbane knowledge of brain anatomy, appropriate training, and care during the whole process; even an untrained clinical researcher will likely fail in differentiating between the lower cerebellum and neighboring veins [25; 26]. Furthermore, manual segmentation of the brain is known to vary among brain anatomy experts and be susceptible to both inter- and intra-variabilities. Thus, manual skull stripping, although doable, is insufficient and inefficient. Therefore, intelligent and effective methods (e.g., semi or fully automatic) are required to address these issues. In the last two decades, numerous research papers that considered the problem of brain extraction and skull stripping in brain MRI have been written and are continually being developed to tackle these limitations and complications. However, they often work well on certain datasets but fail on new or other ones and sometimes involve case-specific parameter tuning. Every algorithm has its constraints, strength, and weakness due to the variability of the dataset of brain MRI. These algorithms can be widely classified into three distinct groups; first is the manual technique, the second group contains classical approaches, and the third group comprises the recent approaches that employed a convolutional neural network (CNN) and deep learning, as displayed in Figure 2.  
As mentioned earlier in the preceding paragraph, generally, manual skull stripping of the brain in MRI volume is treated as the \"ground truth\" or \"gold standard,\" and is commonly utilized to validate other semi-automatic and automatic skull stripping methods. However, manual delineation of the brain MRIs is a laborious and time-consuming process, and not feasible to perform on a huge scale. Classical or conventional approaches mostly comprise the use of low-level image processing techniques; e.g., thresholding, histogram analysis, region growing, mathematical morphology, and edge detection [27]. Figure 2: Skull stripping algorithms can be widely classified into three distinct groups; first is the manual technique, the second group contains classical approaches, and the third group comprises the recent approaches that employed CNN and deep learning. CNN and deep learning-based methods turned into frequently used algorithms to solve neuroimaging problems and challenges [28], after the pioneering CNN \"AlexNet\" on the image recognition by [PERSON] et al. [29].