# Brain Extraction Methods in Neonatal Brain MRI and Their Effects on Intracranial Volumes
## 2 Materials and Methods
### Selection of Automated Brain Extraction Methods  
The criteria for selection of the skull stripping methods were based on their public availability, citations, generalizability for a variety of contrasts, and use of state-of-the-art methods without published studies in neonates. Four automated BE methods were used (Table 2): two conventional methods (Brain Extraction Tool (BET2) [14] and Simple Watershed Scaling (SWS) [42]) and two deep learning methods (HD Brain Extraction Tool (HD-BET) [19] and SynthStrip [26]). Again, each brain mask obtained for each method was also divided into stack of slices, following the same procedure as described in Section 2.3. Finally, whereas the different BE methods can be used within toolboxes or as standalone programs, in this study, the respective toolboxes were used (second column of Table 2), with the exception of SynthStrip [26], which was used as a standalone program running in Docker [43] (the software versions can be found in Section 2.6). BET2 [14] is based on BET [8], one of the most widely used methods for skull stripping, using the original model or several modified versions [6; 7; 9]. BET2 uses techniques such as \begin{table}
\begin{tabular}{c c c} \hline \hline
**Brain Extraction Method** & **Software** & **Method Category [9]** \\ \hline Brain Extraction Tool (BET version 2.0, BET2) [8; 14] & FSL (FMRIB Software Library) [44] & Deformable surface-based model \\ \hline Simple Watershed Scaling (SWS)—unvalidated module in Morphologically Adaptive Neonate Tissue Segmentation (MANTS, version 1.1) toolbox [42] & SPM (Statistical Parametric Mapping) [45] & \\  & & running in MATLAB® [46] or as an & Intensity-based (watershed transform) \\ \hline HD Brain Extraction Tool (HD-BET, version 1.0) [19] & \begin{tabular}{c} Extension of 3D Slicer [38] or as \\ open-source standalone \\ \end{tabular} & \begin{tabular}{c} Deep learning \\ (based on U-Net architecture and its \\ 3D derivatives) \\ \end{tabular} \\ \hline SynthStrip (version 1.3) [26] & \
\begin{tabular}{c} FreeSurfer [47] or as open-source \\ standalone \\ \end{tabular} &
\begin{tabular}{c} Deep learning \\ (based on 3D U-Net) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Selection of automated brain extraction methods. intensity clamping, surface point detection, and mesh fitting to find the brain boundary in brain MRI [14]. Even though they do not have parameters optimized for neonates, they have been used in this context [42; 48; 49; 50; 51; 52; 53; 54]. SWS is a preliminary version of a BE tool available within the Morphologically Adaptive Neonatal Tissue Segmentation (MANTiS) toolbox [42], based on watershed transform. It can be used before the tissue classification pipeline and has been used in several studies [55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65], although it was not used in the validation study of the toolbox (they used BET instead) as it was not available then. HD-BET [19] relies on artificial neural networks and has outperformed six commonly used BE tools (BET [8], 3 dSkullStrip [27], BSE [15], ROBEX [22], BEAST [12], and MONSTR [23]). The algorithm was trained with multi-sequence MRI from adults (precontrast T1w, postcontrast T1w, T2w, and fluid-attenuated inversion recovery (FLAIR)) with different MRI scanners and acquisition parameters and showed robustness even in the presence of pathology- or treatment-induced tissue alterations [19]. SynthStrip [26] builds on a solid foundation laid by prior studies of deep learning algorithms for BE, using a 3D U-Net convolutional architecture. It has generally produced highly accurate brain masks compared with other BE tools (BET [8], BEAST [12], DMBE [17], FSW [18], ROBEX [22], and 3 dSkullStrip [27]). It is a flexible BE tool that can be deployed universally for a variety of brain images because it is agnostic to acquisition parameters, as it never samples any real data during training because a strategy for synthesizing diverse training data is applied.