  Skull Stripping is a requisite preliminary step in most diagnostic
neuroimaging applications. Manual Skull Stripping methods define the gold
standard for the domain but are time-consuming and challenging to integrate
into processing pipelines with a high number of data samples. Automated methods
are an active area of research for head MRI segmentation, especially deep
learning methods such as U-Net architecture implementations. This study
compares Vanilla, Residual, and Dense 2D U-Net architectures for Skull
Stripping. The Dense 2D U-Net architecture outperforms the Vanilla and Residual
counterparts by achieving an accuracy of 99.75% on a test dataset. It is
observed that dense interconnections in a U-Net encourage feature reuse across
layers of the architecture and allow for shallower models with the strengths of
a deeper network.
