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Skull Stripping is a requisite preliminary step in most diagnosticneuroimaging applications. Manual Skull Stripping methods define the goldstandard for the domain but are time-consuming and challenging to integrateinto processing pipelines with a high number of data samples. Automated methodsare an active area of research for head MRI segmentation, especially deeplearning methods such as U-Net architecture implementations. This studycompares Vanilla, Residual, and Dense 2D U-Net architectures for SkullStripping. The Dense 2D U-Net architecture outperforms the Vanilla and Residualcounterparts by achieving an accuracy of 99.75% on a test dataset. It isobserved that dense interconnections in a U-Net encourage feature reuse acrosslayers of the architecture and allow for shallower models with the strengths ofa deeper network.

Performance Evaluation of Vanilla, Residual, and Dense 2D U-Net Architectures for Skull Stripping of Augmented 3D T1-weighted MRI Head Scans