# Conventional and Deep Learning Methods for Skull Stripping in Brain MRI
## 4 CNN-Based or Deep Learning-Based Approaches
### 2D Skull-Stripping Method
The parameter determination for all training is done by the artificial bee colony (ABC) algorithm. The ABC algorithm is an optimization algorithm based on swarm intelligence. They mentioned that the solution of coefficient parameters, templates, and output functions requires a large multidimensional search space, so the authors proposed using the ABC algorithm because it has a wide search space. The mCNN-MRI-SS algorithm shows similar or worse performance than BET and BSE, especially on superior and inferior slices. This may be due to calculation errors in the analysis process. The calculation speed is also slow compared to other algorithms since the analysis algorithm is applied to all particles. The next paper proposed skull stripping using a confidence segmentation convolutional neural network (CSCNet) [114]. CSCNet adopted an encoder-decoder structure. The network is very similar to SegNet [115], one of the most frequently used networks in semantic segmentation. The only difference is that while the authors of SegNet adhered to use the activation function as a softmax function, the author used the ReLU function. This ReLU function is used to produce a confidence level in the pixels of the image. Using the fact that MR images are grayscale, the author created a confidence level matrix as a bitmask and applied it to the original. The reason is that the author's model cannot output accurate brain tissue, so they use the results of the activation function trained based on the target image. In other words, the higher the activated state, the higher the reliability. CSCNet performs better than the other methods, but it is not robust with respect to artifacts. [PERSON] et al. [116] presented a method that combines both the advantages of the active shape model (ASM) and CNN. ASM [117] is a statistical model that finds the desired object through an iterative transformation of data to find the desired object in a new image. ASM is widely used in face image analysis and medical imaging. To proceed with the ASM, the shape of the object should be searched and attribute information indicated by each feature point of the object is required. There are some points worth mentioning in this paper. First, instead of dealing with 3D structures, the brain image is regarded as a continuous form of the sagittal planes which are symmetrical. It allows making more sophisticated segmentation by predicting the second half with data from the first half. The brain is divided into three groups according to the criteria set by the authors. The first group is the smallest but has a very complicated structure, and the third group is the largest. In each group, slices have a very similar form. Having a similar form allows the algorithm to achieve high accuracy segmentation. Second, ASM is used to find the brain boundaries of the image using the prior information of each group. ASM is applied in the second and third groups first. The contour generated from ASM is refined using post-processing techniques, such as a CNN, a conditional random field, and a Gaussian process, and some special other rules. Next, segmentation results of the second group are fed to CNN that processes the first group, making it easier to deal with the first group that is difficult to process. The ASM algorithm, however, only works well when the shape of the test image is similar to the shape of the training image. The skull and the brain look very similar in MRI images, so it is difficult to differentiate them. To solve this problem, [PERSON] et al. [118] created a CompNet network using creative ideas. The network employed encoder-decoder networks. It has two pathways, the first path learning the features of the brain tissue and the second path learning the complementary part located outside the brain. This technique provides a robust solution for brain extraction from MRIs. However, due to the lack of true masks for complementary parts, the authors created a sub-encoder-decoder network that reconstructs the original one based on the output of the two branches.