# Conventional and Deep Learning Methods for Skull Stripping in Brain MRI
## 4 CNN-Based or Deep Learning-Based Approaches
### 3D Skull-Stripping Method  
Unlike the 2D skull-stripping method, which does not utilize the context of adjacent slices, the 3D skull-stripping method can utilize all three-dimensional information, leading to better segmentation results. However, it has the disadvantage of using a large amount of computation. [PERSON] et al. [130] proposed the network structure which consists of seven 3D convolutional hidden layers and one convolutional soft-max output-layer. The network was specially designed only for skull stripping. The authors wanted to cover brains of various ages, brains with artifacts, and brains of various shapes with minimal parameter tuning. Furthermore, the goal was to create a robust network for any single modality or the combination of several modalities (T\({}_{1}\)W, T\({}_{2}\) W, T\({}_{2}\)-FLAIR, etc.). The author tested different structures by adding and removing various layers and found that the proposed structure shows the best performance. When compared with the most popular conventional methods (BET, BSE, Robex, etc.) on IBSR [41], LPBA40 [40], and OASIS [36] datasets, [PERSON]'s [130] method showed the highest performance in terms of dice and specificity but average performance in sensitivity. [PERSON] et al. [93] suggested that 3D-UNet, the general 3D segmentation network, could be used for skull-stripping problems. The authors showed that the performance is comparable to the method developed by [PERSON] et al. [130]. The test result of the NFBS dataset showed that [PERSON] et al. 's network has better performance in terms of dice and specificity and 3D-UNet shows better performance regarding sensitivity. [PERSON] et al. [131] showed a technique that improves the performance by using both the classic method and the deep learning method together. The authors point out that the network has limitations because it learns spatial and contextual information from numerous image patches, and the number of medical images is too small to train the network. To solve those two problems, the author proposed the spatially localized atlas network tiles (SLANT) method. Multiple spatially distributed networks were used to overcome the spatial limitation. Each network was trained from fixed patches. Since multiple networks are used, each network can focus on finding the differences from very similar patches. To enable this strategy, affine registration and intensity normalization were used in advance. Finally, label fusion was used to produce the result from the network tiles. To increase the number of training image sets, the authors made auxiliary labels from 5111 unlabeled scans. The auxiliary labels were made through multi-atlas segmentation. However, the biggest problem with the method is that it requires a lot of computational resources. If only one GPU is used, the training time or the test time increases linearly whenever the network tile increases. [PERSON] et al. [132] created their own network based on U-Net but upgraded its structure. The authors attempted to create a network that is robust to the deformation of the brain tissue by disease or treatment and is not affected by the deformation of MRI hardware. Their modification is as follows: First, preactivation residual blocks were put in the encoder section of U-Net, and the results of the block were concatenated with the original one. It deepens the network architecture and improves gradient flow. The patch size was \(128\times 128\times 128\) voxels and the voxel resolution was \(1.5\times 1.5\times 1.5\) mm\({}^{3}\). It almost covers the entire brain. The large patch size allows the brain mask to be accurately reconstructed, although any part of the brain can be lost due to an accident. Second, they created auxiliary loss layers. The gradients in the deep part of U-Net are very small due to chain rules, making it very difficult to train deep parts. Thus, the authors tried to solve this problem by putting auxiliary loss layers into the deep part. Finally, the authors changed the activation function and the normalization function.