## 4 Discussion  
This study investigated the impact of different intensity processing procedures and brain extraction configurations on the efficacy of automated brain extraction from MRI images of human subjects suffering from traumatic brain injury. In our analyses, we primarily use the mean dice similarity coefficient to evaluate the accuracy of brain extraction pipelines. While other studies evaluating automated brain extraction methods generally report the median, we deliberately report the mean. The frequency distribution of DSC scores across a set of images typically exhibits a left skew, particularly in high-performing brain extraction pipelines. The performance of these pipelines does not decrease linearly but rather tends to drop sharply. Thus, a portion of the data consist of outliers, where the extraction results are exceptionally poor. These very poor extractions have an outsized impact on preprocessing efficiency, as they require extensive manual corrections. Extensive manual corrections, even if only for a few dozen images, necessitate the recruitment of significant auxiliary resources. Large datasets, such as EpiBios4 Rx's, can require the recruitment and training of volunteer researchers. Thus, the poorest extractions introduce the most significant logistical complexities and, in turn, are the greatest burden on the efficiency of the TBI preprocessing pipeline. Reporting the median would obscure these critical outliers, providing a less complete picture of the pipeline's overall performance. On the other hand, using the mean ensures that these critical cases are represented in our results. Our comparison of brain extraction pipelines with optiBET and BET configurations on T1-MPRAGE and T2-FLAIR MRIs produces several critical findings with significant implications for the analysis of patient MRI data, particularly for the precise and efficient preprocessing of subjects with traumatic brain injury. As expected, we found that optiBET performed superior to BET in all pipelines for both MRI types. We found notable differences among the ten different BET configurations, with several results contradicting and significantly adding to previous findings. For both sequence types, we found that BET configurations with a primary option (\"B\" or \"R\") almost always outperformed configurations without a primary option. For T1-MPRAGE specifically, we found that BET1B was the most accurate BET pipeline, followed by BET1R and BET1B,f0.2,g0.3. This contrasts with [PERSON] et al. (2018), who found that BET1B,f0.1 was the highest-performing pipeline for 3D T1 images in multiple sclerosis. Possible reasons for this discrepancy include differences in pathology, the MRI acquisition protocol, procedures for generating gold-standard extractions, and evaluation with the mean vs. median (however, using the median would not change the relative performance of the pipelines for our data). Additionally, because [PERSON] et al. (2018) did not evaluate BET configurations with alterations to option \"g\", BET1B,f0.2,g0.3 would not be accounted for. The divergence in optimal pipeline performance underscores the potential need for tailored preprocessing approaches for TBI data. These findings further prompt a reevaluation of assumptions on the efficacy of preprocessing techniques, which are often based on their performance on datasets with different pathologies. This suggests that a \"one-size-fits-all\" approach to preprocessing patient MRI data may be inappropriate, particularly in complex cases such as TBI, where a technique's effectiveness should be rigorously validated against the specific challenges presented by the pathology. Alternatively, generalizations about technique performance on patient data may be viable, but these assumptions can only be made when they are sourced from investigations on pathologies of equal or greater complexity.