# Conventional and Deep Learning Methods for Skull Stripping in Brain MRI
## 5 Conclusions  
This review article has presented a comprehensive and extensive overview of the state-of-the-art conventional and CNN deep learning-based skull stripping (or whole-brain extraction) methods in brain MRIs. Many of the available approaches employed mainly T\({}_{1}\)W brain MRIs due to good soft-tissue contrast. Even though most of the published algorithms have relatively decent results in the field of neuroimage analysis, there is a certain distance and discontinuity between the research community and clinical applications. In most cases, clinicians and practitioners still depend on manual skull stripping due to the communication gap and lack of interaction between the researchers and \begin{table}
\begin{tabular}{c c c} \hline \hline
**Authors** & **Package Name** & **Link** \\ \hline [PERSON] et al. [50] & BSE (BrainSuite v.19a) & [[http://brainsuite.org/](http://brainsuite.org/)]([http://brainsuite.org/](http://brainsuite.org/)) \\ [PERSON] et al. [99] & ROBEX1.2 & [[https://www.nitrc.org/projects/robex](https://www.nitrc.org/projects/robex)]([https://www.nitrc.org/projects/robex](https://www.nitrc.org/projects/robex)) \\ [PERSON] et al. [65] & _PINCRAM_ & [[http://soundray.org/pincram](http://soundray.org/pincram)]([http://soundray.org/pincram](http://soundray.org/pincram)) \\ [PERSON] [85] & _RGS_1\(1\) & [[http://hdsp.yonsei.ac.kr/pages/download/rgs_1](http://hdsp.yonsei.ac.kr/pages/download/rgs_1)]([http://hdsp.yonsei.ac.kr/pages/download/rgs_1](http://hdsp.yonsei.ac.kr/pages/download/rgs_1))\_1\_zip \\ [PERSON] et al. [81] & MONSTR & [[http://www.nitrc.org/projects/monstr](http://www.nitrc.org/projects/monstr)]([http://www.nitrc.org/projects/monstr](http://www.nitrc.org/projects/monstr)) \\ [PERSON] et al. [110] & CONSNet & [[https://github.com/MICLab-Unicamp/CONSNet](https://github.com/MICLab-Unicamp/CONSNet)]([https://github.com/MICLab-Unicamp/CONSNet](https://github.com/MICLab-Unicamp/CONSNet)) \\ [PERSON] et al. [118] & CompNet & [[https://github.com/SenthLeaser/CNN-Brain-MRI-](https://github.com/SenthLeaser/CNN-Brain-MRI-)]([https://github.com/SenthLeaser/CNN-Brain-MRI-](https://github.com/SenthLeaser/CNN-Brain-MRI-)) \\ [PERSON] et al. [130] & Deep MRI brain extraction & [[https://github.com/GUR9000/Deep](https://github.com/GUR9000/Deep)]([https://github.com/GUR9000/Deep](https://github.com/GUR9000/Deep))\_MRI\_brain\_extraction \\ [PERSON] et al. [131] & SLANT & [[https://github.com/MASILab/SLANTbrainSeg](https://github.com/MASILab/SLANTbrainSeg)]([https://github.com/MASILab/SLANTbrainSeg](https://github.com/MASILab/SLANTbrainSeg)) \\ [PERSON] et al. [132] & HD-BET & [[https://github.com/MICL-DKFZ/HD-BET](https://github.com/MICL-DKFZ/HD-BET)]([https://github.com/MICL-DKFZ/HD-BET](https://github.com/MICL-DKFZ/HD-BET)) \\ [PERSON] et al. [133] & MeshNet & [[https://github.com/ETndoi/MeshNet](https://github.com/ETndoi/MeshNet)]([https://github.com/ETndoi/MeshNet](https://github.com/ETndoi/MeshNet)) \\ [PERSON] et al. [23] & DeepNeuro & [[https://github.com/GITM-Lab/DeepNeuro](https://github.com/GITM-Lab/DeepNeuro)]([https://github.com/GITM-Lab/DeepNeuro](https://github.com/GITM-Lab/DeepNeuro)) \\ [PERSON] et al. [107] & optBiET & [[http://montilab.psych.ucla.edu/fmri-wiki](http://montilab.psych.ucla.edu/fmri-wiki)]([http://montilab.psych.ucla.edu/fmri-wiki](http://montilab.psych.ucla.edu/fmri-wiki)) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Publicly available conventied and deep learning packages. practitioners. The objective of several skull stripping tools is to do only the research, and they are barely useful for clinicians. Therefore, embedding the developed tools into more user-friendly environments will become inevitable in the future. Consequently, incorporating all the accessible developed skull stripping into a more intelligible and easier-to-use environment will become unavoidable in the future.