论文标题
用于增强3D T1加权MRI头扫描的颅骨剥离的香草，残留和密集的2D U-NET架构的性能评估
Performance Evaluation of Vanilla, Residual, and Dense 2D U-Net Architectures for Skull Stripping of Augmented 3D T1-weighted MRI Head Scans
论文作者
论文摘要
在大多数诊断神经影像应用中，头骨剥离是必不可少的初步步骤。手动头骨剥离方法定义了该域的黄金标准，但耗时且挑战，可以集成到具有大量数据样本的处理管道中。自动化方法是用于Head MRI细分的研究领域，尤其是深度学习方法，例如U-NET体系结构实现。这项研究比较了用于颅骨剥离的香草，残留和密集的2D U-NET架构。密集的2D U-NET体系结构在测试数据集上的准确度达到99.75％，超过了香草和残留物。据观察，U-NET中的密集互连鼓励在体系结构的层次上重新使用功能，并允许具有更深层网络的优势的较浅模型。
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.
