# Conventional and Deep Learning Methods for Skull Stripping in Brain MRI
## 4 CNN-Based or Deep Learning-Based Approaches
### 2D Skull-Stripping Method  
2D semantic segmentation predicts the total volume by segmenting each slice independently and by combining the results of 2D segmentation. Generally, 2D semantic segmentation is faster than 3D segmentation and there are many techniques for it compared to 3D semantic segmentation. Because it is based on the 2D slice as input, it does not take advantage of the context of adjacent slices. In order to overcome this limitation, 2D skull-stripping methods making use of adjacent information have been introduced and some of them showed similar performances to the 3D based methods with less computation. [PERSON] et al. [109] proposed two methods: a voxel-wise network and a fully convolutional network. The first is a parallel voxel-wise network and the second is a parallel 2D FCN U-Net. Each network is followed by an auto-context CNN classifier. It was named Auto-Net. The authors pointed out that the biggest problem with 3D CNN is its expensive computation and proposed to use two networks. The first network consists of three 2D networks (axial, coronal, and sagittal) instead of the computationally expensive 3D CNN. The 2D network receives the input image in three different sized patches. The reason for using three different patches is to get both local and global information. This network has much reduced the number of parameters compared to 3D CNN while showing as good a performance as 3D CNN does. The second proposed network is a fully convolutional network based on U-Net [108]. It integrates the contextual information by combining the low-level appearance feature and the high-level appearance feature from an auto-context algorithm. Auto-Net needs a trained model in each plane to utilize auto-context, so it is not an end-to-end form. [PERSON] et al. [110] proposed a network consisting of two main parts. The first part is a network having three 2D CNNs, and the second part is a network handling context variation. The three 2D CNNs handle three planes of space (axial, coronal, and sagittal). The authors call this CONSNet. The authors showed a better performance than the one-way process by adopting this tri-planar approach. 3D segmentation is achieved by reconstruction of the output of 2D prediction through concatenation. Interestingly, the authors used so-called silver standard masks instead of gold-standard masks as ground truth. Silver standard masks mean the labels created through consensus that are the results of publicly available non-deep learning-based skull-stripping methods. Gold standard masks are manual masks created by experts. In the paper, the authors reported that the proposed method outperformed in the LPBA40 and OASIS datasets, despite training with only the CC-359 dataset. Using silver-standards is a big advantage because it significantly reduces the cost of manual annotation. Silver-standards masks were obtained by forming a consensus from eight automated skull-stripping methods. [PERSON] et al. [111] also published papers using 2D FCN U-Net. The authors used silver standard masks to train the model and claimed that it showed comparable generalization performance when compared to gold standard masks. Furthermore, the results show that silver standard masks can be used to augment the data to reduce the need for manual segmentation. Inspired by this paper, [PERSON] et al. [112] performed hippocampus segmentation on three planes using 2D FCN U-Net. However, they have proposed a better way than adding additional networks to get a final consensus. They added the activation heatmap to each 2D FCN U-Net, applied a predefined threshold, and performed 3D labeling. This method showed much better performance than that of [PERSON] et al. [111]. [PERSON] et al. [113] performed skull stripping using a cellular neural network called the multistable cellular neural network on MRI for skull stripping (mCNN-MRI-SS). They proposed using contrast enhancement for preprocessing by a linear image combinations algorithm. This leads to less noise and improved contrast results.