# Conventional and Deep Learning Methods for Skull Stripping in Brain MRI
## 4 CNN-Based or Deep Learning-Based Approaches
### 3D Skull-Stripping Method
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Deep-Learning** & **Authors** & **Year** & **Methods** & **Dataset** & **Performance Measures** \\ \hline \multirow{4}{*}{2D Convolutional Neural Network} & [PERSON] et al. [109] & 2017 & Auto-Net & T\({}_{1}\)W, T\({}_{2}\)W, fatal T\({}_{2}\)W & Dice, Sensitivity, Specificity \\  & [PERSON] et al. [110] & 2019 & CONSNet & T\({}_{1}\)W & Dice, Sensitivity, Specificity, \\  & [PERSON] et al. [113] & 2018 & mCNN-MRI-SS & T\({}_{1}\)W, T\({}_{2}\)W & Dice, Sensitivity, Specificity, \\  & [PERSON] et al. [114] & 2018 & CSCNet & FLAIR & Dice, Sensitivity, Specificity \\  & [PERSON] et al. [116] & 2018 & ASM-CNN & T\({}_{1}\)W & Dice, Sensitivity, Specificity, \\  & [PERSON] et al. [118] & 2018 & CompNet & T\({}_{1}\)W & HD, Jaccard \\  & [PERSON] et al. [119] & 2018 & NeuronNet & T\({}_{1}\)W, T\({}_{2}\)W & Dice \\ \hline \multirow{4}{*}{3D Convolutional Neural Network} & [PERSON] et al. [130] & 2016 & Deep MRI & T\({}_{1}\)W, FLAIR & Dice, Sensitivity, Specificity \\  & [PERSON] et al. [131] & 2019 & SLAMT\({}_{1}\)W & Dice, SSSD \\ \cline{1-1}  & [PERSON] et al. [132] & 2019 & HD-BET & T\({}_{1}\)W, cf\({}_{1}\)W, FLAIR & Dice, HD \\ \cline{1-1}  & [PERSON] et al. [133] & 2017 & MeshNet & T\({}_{1}\)W, T\({}_{1}\)W IR, FLAIR & Dice, Sensitivity, Specificity, \\ \cline{1-1}  & [PERSON] et al. [134] & 2019 & FRnet & Infant T\({}_{1}\)W & Dice \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Notable algorithms of deep learning-based methods. generally downsample and upsample using pooling, while V-Net downsamples and upsamples using convolution layers with strides. VoxResNet [138] consists of 25 layers, including the VoxRes module. The VoxRes module corresponds to ResNet's [139] residual unit in three dimensions. Here, the deconvolution of the output of every fourth block is performed to classify and merge the result to get the final output. The advantage of this approach is the integration of multimodality and multilevel contextual information. As a result, the overall information can be learned and the functions of various scales can be utilized, resulting in increased segmentation performance. DenseVoxNet [140] was proposed. This network outperforms 3D U-Net and VoxResNet even with fewer parameters. Similar to VoxResNet, the network consists of a DenseBlock, which extends the dense connections of DenseNet [141] in three dimensions. The benefits of DenseBlock include less information loss, a lesser gradient vanishing problem, and fewer learning parameters. Although deep learning-based methods revolutionized image segmentation by automatically learning the most difficult feature representations from data, they still have drawbacks. One of the major drawbacks of deep learning methods is the inability to show how to get the result. Deep learning models are basically black boxes. This opacity of deep learning makes deep learning difficult to understand or difficult to improve. As a result, developers do not know how to adjust the numerous hyperparameters of a deep learning model and how to improve the performance of the model. Unfortunately in many cases, they have no choice but to rely on trial and error, which are very inefficient. Another problem is the limitation of data-dependence. Basically, a lot of data is needed to properly train a deep learning network model. This is a well-known characteristic of deep learning methods. For this purpose, various open databases for general images have been built and keep accumulating data. However, this is not a simple problem for medical imaging, because collecting lots of annotated datasets in medical images is often very difficult, time-consuming, and expensive, not to mention the ethical concerns and privacy issues. Global collaboration is the only way to address this data issue wisely and effectively. Finally, Table 5 summaries publicly available packages with their respective online links. Users can download and execute their packages from the links.