标题： 全局上下文是并行高效纤维束成像分割所需的一切 显示英文标题

标题： Global Context Is All You Need for Parallel Efficient Tractography Parcellation

摘要： 弥散MRI中的全脑追踪通常会接着进行分割，其中每条轨迹线被分类为属于特定的白质束，或者被视为假阳性而被丢弃。高效的分割在大规模研究中非常重要，这些研究需要处理大量数据；同样，在临床环境中也非常重要，因为计算资源往往有限。 TractCloud 是一种最先进的方法，旨在通过局部-全局表示来最大化准确性。 我们证明了局部上下文对这种方法的准确性没有贡献，甚至在处理病理病例时是有害的。 基于这一观察，我们提出了 PETParc，这是一种新的并行高效轨迹分割方法。 PETParc 是一种基于 Transformer 的架构，其中全脑轨迹图被随机划分为子轨迹图，其轨迹线并行分类，同时作为彼此的全局上下文。 这使得相对于 TractCloud 的速度提高了两个数量级，并且即使在没有 GPU 的临床工作站上也可以进行推断。 PETParc 通过一种新颖的翻转不变嵌入，或者简单地将翻转用作数据增强的一部分，来考虑轨迹线方向的缺失。 尽管速度加快，但结果往往比以前的方法更好。 代码和预训练模型将在接受后公开。 ``` 显示英文摘要

摘要： Whole-brain tractography in diffusion MRI is often followed by a parcellation in which each streamline is classified as belonging to a specific white matter bundle, or discarded as a false positive. Efficient parcellation is important both in large-scale studies, which have to process huge amounts of data, and in the clinic, where computational resources are often limited. TractCloud is a state-of-the-art approach that aims to maximize accuracy with a local-global representation. We demonstrate that the local context does not contribute to the accuracy of that approach, and is even detrimental when dealing with pathological cases. Based on this observation, we propose PETParc, a new method for Parallel Efficient Tractography Parcellation. PETParc is a transformer-based architecture in which the whole-brain tractogram is randomly partitioned into sub-tractograms whose streamlines are classified in parallel, while serving as global context for each other. This leads to a speedup of up to two orders of magnitude relative to TractCloud, and permits inference even on clinical workstations without a GPU. PETParc accounts for the lack of streamline orientation either via a novel flip-invariant embedding, or by simply using flips as part of data augmentation. Despite the speedup, results are often even better than those of prior methods. The code and pretrained model will be made public upon acceptance.