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.

翻译：在大多数诊断性神经成像应用中,Skull脱钩是必要的第一步。手动Skull脱钩方法为域界定了金标准,但在纳入含有大量数据样本的加工管道方面却耗费时间且具有挑战性。自动方法是首席磁共振分解的一个积极研究领域,特别是U-Net结构实施等深层学习方法。这项研究比较了Vanilla、遗留物和Dense 2D U-NetSkull脱钩结构。Dense 2D U-Net结构在测试数据集上达到99.75%的精确度,从而超越了Vanilla和残余物的对等结构。观察到,U-Net的稠密连接鼓励了结构各层的特征再利用,并使得更浅的模型具有更深的网络的优势。