# Conventional and Deep Learning Methods for Skull Stripping in Brain MRI
## 4 CNN-Based or Deep Learning-Based Approaches
### 2D Skull-Stripping Method
Sub-encoder-decoder networks provide direct feedback to networks processing brain parts and complementary parts to reconstruct the original one. Optimal CompNet shows higher accuracy when compared with one of the most popular networks such as U-Net and dense U-Net. NeuroNet [119] is a network that mimics state-of-the-art brain segmentation tools such as FSL [120], SPM [121], and others [122]. The network was trained from 5000 T\({}_{1}\)-weighted brain MRI scans that were automatically segmented from standard neuroimaging pipelines (FSL, SPM, and others). The structure of the network is as follows. There is one encoder structure and multiple decoders that receive the output of the encoder and extract the final result. Each decoder teaches the state-of-the-art brain segmentation tool. Generating multiple outputs from a single model can be very powerful due to overlapping label maps. Besides, it is very efficient because it outputs several results at once. The authors also asserted that networks are very robust from changes in input data. Another advantage is that Neuronet does not require typical preprocessing. In other words, the network can produce output from raw images and no additional hyper-parameters are required. Next, we briefly introduce the deep learning techniques that are widely used as general 2D semantic segmentation and therefore can be applicable to the skull-stripping problem. Fully Convolutional networks for semantic segmentation (FCN) [123] showed high improvements in the field of semantic segmentation. What differentiates FCN from previous semantic segmentation papers is that the fully connected layer is removed. Existing papers had the problem that the location information was removed through the fully connected layer. Therefore, the authors of FCN replaced the fully connected layer with a \(1\times 1\) convolution. Next, upsampling was performed to restore the original size of the image. Instead of performing interpolation, the output from the encoder is combined after interpolation to obtain the result of segmentation in detail. SegNet [115] came out in 2015. This network is similar to FCN in that it uses encoder-decoder architectures. However, the key difference is that more shortcut connections are used, and instead of copying encoder functionality as in FCN, Maxpooling's indices are transitioned to increase memory efficiency. U-Net [108] also forms a structure similar to the previous FCN and SegNet. U-Net is divided into contracting path and expanding path. The contracting path is used to identify the global context and the expanding path is used to identify the local details. The key point of U-Net is that it allows for more accurate localization by concatenating the output of the contracting path prior to each upconvolution of the expanding path. As a way to solve the semantic segmentation problem, the key point of DeepLab is using atrous convolution. DeepLab V1 [124] first applied atrous convolution for semantic segmentation. Atrous convolution is the novel method which makes the stride between elements of a convolution filter. As the distance gets longer, the filter can cover a wider field of view with the same number of parameters. In general, since the size of the receptive field determines the performance of semantic segmentation, it is a big advantage to have a wide receptive field without increasing the amount of computation. DeepLab V2 [125] proposed the atrous spatial pyramid pooling (ASPP) technique for applying multiscale contexts. The technique performs atrous convolution over various distances and then concatenates the results. This technique allows for identifying all local and global features, making it possible to identify multiscale contexts accurately. DeepLab V3 [126] proposed a more dense feature map by using an atrous convolution in existing ResNet structures. DeepLab V3+ [127] proposed the use of atrous separable convolution, which combines separable convolution and atrous convolution.