# Conventional and Deep Learning Methods for Skull Stripping in Brain MRI
## 3 Classical or Conventional Skull Stripping Approaches
### Meta-Algorithms and Hybrid Methods  
An individual algorithm often will not sufficiently perform the whole brain extraction or skull stripping in every subject across an entire brain MRI dataset. Specifically, many different procedures must be tried or manual intervention utilized to attain satisfactory results. A simple way to access and test various approaches is an environment that presents many similar algorithms. A meta-algorithm or hybrid method that allows the requirement of a general procedure and achieves a valid result, regardless of input data, would permit the task of interpreting the results from many algorithms and selecting the best procedures to be fully automated. Each of the aforementioned skull stripping algorithms for brain MRIs possesses pros and cons that alter with the image characteristics; scanning protocol, such as image signal-to-noise ratio, contrast, and resolution; and subject-specific characteristics, such as age and atrophy ([PERSON] et al., 2016; [PERSON] et al., 2016). Furthermore, the algorithms can also deviate in their accuracy in different brain anatomic regions ([PERSON] et al., 2016). The development of a hybrid algorithm that intelligently exploits theadvantages of the contributing sub-algorithms should attain brain extraction results that are, typically, superior to any individual method. Hybrid methods integrate the advantages of two or more approaches to enhance the precision and accuracy of skull stripping in brain MRIs. For example, [PERSON] et al. [94; 95] combined atlas-based geodesic active contour brain segmentation with the level set-based algorithm and implemented it using the Insight Segmentation and Registration Toolkit (ITK) which they named \"itk::Strip TsImageFilter.\" This hybrid algorithm successfully segments the brain in T\({}_{1}\)-contrast, T\({}_{1}\)W, and T\({}_{2}\)W-MRIs; computed tomography (CT); and FLAIR images. [PERSON] et al. [96] developed the brain extraction meta-algorithm (BEMA) by executing four different brain extractors (including BSE [50], BET [25], 3 dIntracrnial [97], and Watershed from FreeSurfer [66]) and a registration procedure in parallel to integrate the brain extraction results in an intelligent way and achieve improved results over any of the individual algorithms. The algorithm performs a voxel-wise analysis of training data to find the optimal Boolean grouping of extraction algorithms to generate the most precise segmentation results for a given voxel. [PERSON] et al. [69] combined a deformable surface model [66] and the watershed algorithm [89] to form a new hybrid watershed algorithm (HWA) for brain segmentation. The deformable surface model effectively incorporates the geometric information of the brain MRI but cannot have access to the intensity information of the interior region of the brain. Similarly, watershed algorithms ignore the geometric information. By combining both approaches, their hybrid algorithm obtained improved results over either one individually. Another hybrid method based on the expectation-maximization algorithm, mathematical morphology, connected component analysis, and geodesic active contours was presented by [PERSON] et al. [98]. A well-known learning-based algorithm \"robust brain extraction (ROBEX)\" was presented by [PERSON] et al. [99]. It incorporates a discriminative (random forest) and a generative model (point distribution) with the graph cuts for whole-brain extraction. The brain boundary is identified using a random forest (RF) classifier. A triangular mesh, constrained by a point distribution model, is evolved to fit the probabilistic output of RF. The deformation (contour) is refined and optimized using graph cut. It exhibited improved performance without any parameter settings. ROBEX is publicly available as the \"ROBEX1.2\" package available at [100]. [PERSON] et al. [101] extend the ROBEX skull stripping method and presented a technique to extract the brain from glioblastoma multiform (GBM) images.