# Conventional and Deep Learning Methods for Skull Stripping in Brain MRI
## 4 CNN-Based or Deep Learning-Based Approaches
### 3D Skull-Stripping Method
The authors found the ReLU function dying in the deep networks, so they replaced it with the leakyReLU function. In the proposed method, the batch size is smaller than other methods. Therefore, the batch means and the standard deviation are unstable. For this reason, the authors applied instance normalization instead of batch normalization. Instance normalization is that each batch normalizes independently from the other batch. Although [PERSON]'s method was applied to brains with tumors or normal brains, their HD-BET algorithm is expected to also work in a wide range of diseases in neuroradiiology. [PERSON] et al. [133] introduced a deep learning model based on volumetric dilated convolutions. Dilated convolutions is another name for atrous convolutions introduced by Deeplab [124]. Compared to other models, this model significantly reduced the number of parameters, shortening the processing time and showing high accuracy for test data. Another important characteristic of this model was that it showed high accuracy even when trained on imperfect data. FRnet [134] offers novel methodologies in three ways. First, the authors created it based on U-Net. The difference between FRnet and U-Net is that it executes residual block after a \"de-convolution + batch normalization + ReLU\" block in the decoder part. It is also novel to apply convolution before concatenation. Another difference is that a new loss function was used to highlight the boundary regions that are ambiguous and blurry. The loss function is weighted at the boundary region since the boundary is always the most important part of the segmentation map. Finally, [PERSON] et al. [23] introduced a deep learning framework that puts the best fit deep learning algorithms in order to deal with neuroimaging practically. The authors noted that many packages are very good at sharing and designing deep learning algorithms, but few provide utilities for working with clinical data. The authors showed how the framework designs and trains the network, and how to modify the state-of-the-art architecture in a flexible and intuitive way using their toolbox. In addition, by providing preprocessing and post-processing which are frequently used in medical imaging, the framework has shown a consistent performance in various environments. Once the author develops a GUI interface for users with no coding capability, it is expected to be more helpful. Table 4 summarizes deep learning-based methods that have been described in the preceding sections. In the next part, we briefly introduce deep learning-based techniques that have been recently proposed for 3D semantic segmentation, and are applicable to our 3D skull-stripping problem. The first network is 3D U-Net [135]. This network is based on a model created by extending one dimension on the existing 2D U-Net. Not only were the methods used in 2D U-Net used for 3D, but they also added techniques such as batch normalization [136]. The strength of this network is that it can learn from sparsely annotated volumetric images. Similar to 3D U-Net, V-Net [137] is a network that has end-to-end structures. It is composed of encoder and decoder. The existing networks