论文标题
Streamnet：白质流线分析的WAE
StreamNet: A WAE for White Matter Streamline Analysis
论文作者
论文摘要
我们提出了StreamNet，这是一种自动编码器体系结构，用于分析大量白质流线的高度异质几何形状。该提出的框架利用了Wasserstein-1度量的几何形状赋值特性，以实现整个流线束的直接编码和重建。我们表明，该模型不仅可以准确捕获人群中流线的分布结构，而且还能够在真实和合成流线之间实现出色的重建性能。使用最新的ART捆绑捆绑捆绑指标对40个健康对照组的T1加权扩散成像产生的白质流线评估了实验模型性能，该指标测量了纤维形状的相似性。
We present StreamNet, an autoencoder architecture for the analysis of the highly heterogeneous geometry of large collections of white matter streamlines. This proposed framework takes advantage of geometry-preserving properties of the Wasserstein-1 metric in order to achieve direct encoding and reconstruction of entire bundles of streamlines. We show that the model not only accurately captures the distributive structures of streamlines in the population, but is also able to achieve superior reconstruction performance between real and synthetic streamlines. Experimental model performance is evaluated on white matter streamlines resulting from T1-weighted diffusion imaging of 40 healthy controls using recent state of the art bundle comparison metric that measures fiber-shape similarities.
