Combined tract segmentation and orientation mapping for bundle-specific tractography
Combined tract segmentation and orientation mapping for bundle-specific tractography
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DOI：
10.1016/j.media.2019.101559
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发表时间：
2019-12-01
影响因子：
10.9
通讯作者：
Maier-Hein, Klaus H.
中科院分区：
文献类型：
--
作者：
Wasserthal, Jakob;Neher, Peter F.;Maier-Hein, Klaus H.
虽然主要的白质束是许多神经科学和医学研究的兴趣所在，但从弥散性MRI束图中对其进行大规模的人工解剖是耗时的，需要专业知识，而且很难复制。在以前的工作中，我们提出了束定向映射（TOM）作为束特异性束束造影的新概念。它基于从原始纤维取向分布函数（FOD）峰到通道特定峰的学习映射，称为通道取向映射。每个区域方向图代表一个区域的体素方向。在这里，我们提出了该方法的扩展，将TOM与束轮廓及其开始和结束区域的准确分割相结合。我们还引入了一种自定义概率跟踪算法，该算法从高斯分布中采样，以每个峰值为中心，具有固定的标准差，从而能够在道路方向图上实现比确定性跟踪更完整的跟踪。这些扩展支持以以前未见过的精度自动创建特定于bundle的图。我们在高质量、低质量和虚幻数据的72个不同束上展示了我们的方法比7种最先进的基准方法运行得更快，产生更准确的束特定束束图，同时避免了繁琐的处理步骤，如全脑束图、非线性配准、聚类或手动解剖。此外，我们在17个数据集上表明，我们的方法可以很好地推广到使用不同扫描仪和设置以及病理获取的数据集。我们的方法的代码可以在https://github.com/MIC-DIGZ/TractSeg上公开获得。(C) 2019作者。Elsevier B.V.出版
While the major white matter tracts are of great interest to numerous studies in neuroscience and medicine, their manual dissection in larger cohorts from diffusion MRI tractograms is time-consuming, requires expert knowledge and is hard to reproduce. In previous work we presented tract orientation mapping (TOM) as a novel concept for bundle-specific tractography. It is based on a learned mapping from the original fiber orientation distribution function (FOD) peaks to tract specific peaks, called tract orientation maps. Each tract orientation map represents the voxel-wise principal orientation of one tract. Here, we present an extension of this approach that combines TOM with accurate segmentations of the tract outline and its start and end region. We also introduce a custom probabilistic tracking algorithm that samples from a Gaussian distribution with fixed standard deviation centered on each peak thus enabling more complete trackings on the tract orientation maps than deterministic tracking. These extensions enable the automatic creation of bundle-specific tractograms with previously unseen accuracy.We show for 72 different bundles on high quality, low quality and phantom data that our approach runs faster and produces more accurate bundle-specific tractograms than 7 state of the art benchmark methods while avoiding cumbersome processing steps like whole brain tractography, non-linear registration, clustering or manual dissection. Moreover, we show on 17 datasets that our approach generalizes well to datasets acquired with different scanners and settings as well as with pathologies. The code of our method is openly available at https://github.com/MIC-DIGZ/TractSeg. (C) 2019 The Author(s). Published by Elsevier B.V.