TractCloud：基于新型局部-全局流线点云表示的无配准纤维束追踪分区方法
TractCloud: Registration-free tractography parcellation with a novel local-global streamline point cloud representation
AI总结：
针对现有纤维束追踪分区依赖配准、忽略流线间几何关系的问题，提出无配准框架TractCloud，采用新型可学习局部-全局流线表示，在多数据集上性能优于现有方法，适用于大规模数据分析。
摘要：
扩散磁共振成像（diffusion MRI）纤维束追踪分区将流线分类为解剖学纤维束，为临床和科研应用提供量化与可视化支持。现有纤维束追踪分区方法高度依赖配准，但配准误差会影响分区效果，且大规模数据集的配准计算成本极高。近年来，研究者提出了基于深度学习的纤维束追踪分区方法，采用多种类型的流线表示方式，但这些方法仅关注单条流线的信息，忽略了脑内流线之间的几何关系。我们提出TractCloud，这是一种无配准框架，可直接在个体被试空间内完成全脑纤维束追踪分区。我们提出了一种新型可学习的局部-全局流线表示方法，利用邻近平行流线与全脑流线的信息，描述大脑的局部解剖结构与全局姿态。我们在一个大规模标注纤维束追踪数据集上训练该框架，并通过施加旋转、缩放、平移等合成变换对数据集进行增强。我们在5个独立采集的、覆盖不同人群与健康状态的数据集上测试了该框架。在所有测试数据集上，TractCloud的性能均显著优于多种当前最优（state-of-the-art）方法。TractCloud无需配准，即可在全生命周期（从新生儿到老年被试，包括脑肿瘤患者）中实现高效、一致的全脑白质分区。TractCloud的鲁棒性与高推理速度使其适用于大规模纤维束追踪数据分析。我们的项目页面位于：https://tractcloud.github.io/。
英文摘要：
Diffusion MRI tractography parcellation classifies streamlines into anatomical fiber tracts to enable quantification and visualization for clinical and scientific applications. Current tractography parcellation methods rely heavily on registration, but registration inaccuracies can affect parcellation and the computational cost of registration is high for large-scale datasets. Recently, deep-learning-based methods have been proposed for tractography parcellation using various types of representations for streamlines. However, these methods only focus on the information from a single streamline, ignoring geometric relationships between the streamlines in the brain. We propose TractCloud, a registration-free framework that performs whole-brain tractography parcellation directly in individual subject space. We propose a novel, learnable, local-global streamline representation that leverages information from neighboring and whole-brain streamlines to describe the local anatomy and global pose of the brain. We train our framework on a large-scale labeled tractography dataset, which we augment by applying synthetic transforms including rotation, scaling, and translations. We test our framework on five independently acquired datasets across populations and health conditions. TractCloud significantly outperforms several state-of-the-art methods on all testing datasets. TractCloud achieves efficient and consistent whole-brain white matter parcellation across the lifespan (from neonates to elderly subjects, including brain tumor patients) without the need for registration. The robustness and high inference speed of TractCloud make it suitable for large-scale tractography data analysis. Our project page is available at https://tractcloud.github.io/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
