# Conventional and Deep Learning Methods for Skull Stripping in Brain MRI
## 3 Classical or Conventional Skull Stripping Approaches
### Atlas or Library-Based Methods  
Atlas or library-based methods achieve brain labeling on the brain images using expert delineations of the brain MRIs. The labeled images subset is known as the atlas, training, or library set. These approaches rely on the target brain images being adequately analogous to the training set for the algorithm to transfer expert knowledge. Unlike other skull stripping methods, these algorithms often require significant preprocessing, such as intensity and spatial normalization [65]. These algorithms can isolate the brain from non-brain tissues when there is no well-defined relationship present between the respective brain region and the skull pixels' intensities in the brain MRIs. [PERSON] et al. [66] designated a brain segmentation algorithm as a preprocessing step in the cortical surface reconstruction process by applying a tessellated ellipsoidal template. [PERSON] et al. [67] proposed a template library-based segmentation technique called multiple-atlas propagation and segmentation (MAPS) that produces a segmentation of the brain utilizing a template library and non-linear atlas registration. It generates multiple segmentations from the best-matched templates with a manually segmented library and integrates them using an algorithm known as simultaneous truth and performance level estimation (STAPLE). The method has been developed and validated against manual measures employing subsets from the Alzheimer's Disease Neuroimaging Initiative(ADNI). [PERSON] et al. [68] also presented a method named \"Brain MAPS\" that compares the brain segmentation of the template library-based technique with BET [25], BSE [50], and HWA [69]. Similarly, [PERSON] et al. [70] developed BEAST, an automatic brain extraction technique, based on nonlocal segmentation incorporated in a multiresolution framework. Principally, this method is inspired by patch-based segmentation [71] where the sum of squared differences (SSD) has been exploited as the metric for the distance between patches. BEAST is much faster than the other label fusion methods and needed a smaller library of priors. Only a library of 50 priors was semi-automatically created from the data in the National Institutes of Health Pediatric Database (NIHPD) [72] the International Consortium for Brain Mapping (ICBM) database [73], and the Alzheimer's Disease Neuroimaging Initiative (ADNI) [74]. The intensity and spatial normalization were performed before constructing the library of priors. The ground truth (or gold standard) of brain masks for library priors have been constructed using a semi-automatic method that includes manual correction. To enhance accuracy while reducing the computation time, an improved nonlocal label fusion scheme based on BEAST is proposed by [PERSON] et al. [75]. A new multi-atlas library construction pipeline has been introduced for better normalization between the template library subjects. The addition of bilateral patch similarity measure, a blockwise labeling approach, and a regularization constraint have aided the methods to increase accuracy and significant savings in computational cost. Multi-atlas skull stripping has been presented in [76]. It is based on a multi-atlas registration framework to confront the challenges originating from the variability of imaging characteristics among different studies. After choosing a set of templates, they used a study-specific template selection strategy that best constitutes the anatomical variation within the dataset. To overcome the problems associated with the registration of brain images with the skull, an adapted registration algorithm was considered which adaptively aligned different regions of brains based on similarity and reliability of matching measures. In the last step, a spatially adaptive weighted voting strategy (ranking of Jacobian determinant values) has been used for joining the co-registered template masks. [PERSON] et al. [65] developed _pincram_, an automatic, versatile algorithm for accurately labeling 3D T\({}_{1}\)W brain MRIs in an adult.