  Accurate skull-stripping is crucial preprocessing in dynamic susceptibility
contrast-enhanced perfusion magnetic resonance data analysis. The presence of
non-brain tissues impacts the perfusion parameters assessment. In this study,
we propose different integration strategies for the spatial and channel squeeze
and excitation attention mechanism into the baseline U-Net+ResNet neural
network architecture to provide automatic skull-striping i.e., Standard scSE,
scSE-PRE, scSE-POST, and scSE Identity strategies of plugging of scSE block
into the ResNet backbone. We comprehensively investigate the performance of
skull-stripping in T2-star weighted MR images with abnormal brain anatomy. The
comparison that utilizing any of the proposed strategies provides the
robustness of skull-stripping. However, the scSE-POST integration strategy
provides the best result with an average Dice Coefficient of 0.9810.
