论文标题
Supwma：深度学习的浅表白质的一致，有效的拖拉术
SupWMA: Consistent and Efficient Tractography Parcellation of Superficial White Matter with Deep Learning
论文作者
论文摘要
白质拟层将拖拉机分类为簇或解剖学上有意义的区域，以实现定量和可视化。大多数分析方法都集中在深层白质（DWM）上，而由于其复杂性，较少的方法解决了浅表白质（SWM）。我们提出了一个基于深度学习的框架，即表面白质分析（SUPWMA），该框架对全脑拖拉机的198个SWM群集进行了有效且一致的分析。基于点云的网络已修改为我们的SWM分析任务，并监督的对比学习可以在合理的流线和离群值之间进行更多的歧视性表示。我们在带有地面真相标签的大型拖拉数据集上进行评估，并在各个年龄段和健康状况的个人的三个独立获取的测试数据集上进行评估。与几种最先进的方法相比，SupWMA获得了高度一致，准确的SWM分析结果。另外，SUPWMA的计算速度比其他方法快得多。
White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts to enable quantification and visualization. Most parcellation methods focus on the deep white matter (DWM), while fewer methods address the superficial white matter (SWM) due to its complexity. We propose a deep-learning-based framework, Superficial White Matter Analysis (SupWMA), that performs an efficient and consistent parcellation of 198 SWM clusters from whole-brain tractography. A point-cloud-based network is modified for our SWM parcellation task, and supervised contrastive learning enables more discriminative representations between plausible streamlines and outliers. We perform evaluation on a large tractography dataset with ground truth labels and on three independently acquired testing datasets from individuals across ages and health conditions. Compared to several state-of-the-art methods, SupWMA obtains a highly consistent and accurate SWM parcellation result. In addition, the computational speed of SupWMA is much faster than other methods.
