Sharmin Nusrat, Olivetti Emanuele, Avesani Paolo
NeuroInformatics Laboratory, Bruno Kessler Foundation, Trento, Italy.
Center for Mind and Brain Sciences, University of Trento, Trento, Italy.
Front Neurosci. 2018 Feb 6;11:754. doi: 10.3389/fnins.2017.00754. eCollection 2017.
Diffusion magnetic resonance imaging (dMRI) allows to reconstruct the main pathways of axons within the white matter of the brain as a set of polylines, called streamlines. The set of streamlines of the whole brain is called the tractogram. Organizing tractograms into anatomically meaningful structures, called tracts, is known as the tract segmentation problem, with important applications to neurosurgical planning and tractometry. Automatic tract segmentation techniques can be unsupervised or supervised. A common criticism of unsupervised methods, like clustering, is that there is no guarantee to obtain anatomically meaningful tracts. In this work, we focus on supervised tract segmentation, which is driven by prior knowledge from anatomical atlases or from examples, i.e., segmented tracts from different subjects. We present a supervised tract segmentation method that segments a given tract of interest in the tractogram of a new subject using multiple examples as prior information. Our proposed tract segmentation method is based on the idea of streamline correspondence i.e., on finding corresponding streamlines across different tractograms. In the literature, streamline correspondence has been addressed with the nearest neighbor (NN) strategy. Differently, here we formulate the problem of streamline correspondence as a linear assignment problem (LAP), which is a cornerstone of combinatorial optimization. With respect to the NN, the LAP introduces a constraint of one-to-one correspondence between streamlines, that forces the correspondences to follow the local anatomical differences between the example and the target tract, neglected by the NN. In the proposed solution, we combined the Jonker-Volgenant algorithm (LAPJV) for solving the LAP together with an efficient way of computing the nearest neighbors of a streamline, which massively reduces the total amount of computations needed to segment a tract. Moreover, we propose a ranking strategy to merge correspondences coming from different examples. We validate the proposed method on tractograms generated from the human connectome project (HCP) dataset and compare the segmentations with the NN method and the ROI-based method. The results show that LAP-based segmentation is vastly more accurate than ROI-based segmentation and substantially more accurate than the NN strategy. We provide a Free/OpenSource implementation of the proposed method.
扩散磁共振成像（dMRI）能够将大脑白质内轴突的主要路径重建为一组折线，即流线。整个大脑的流线集合称为纤维束图。将纤维束图组织成具有解剖学意义的结构，即纤维束，这就是纤维束分割问题，在神经外科手术规划和纤维束测量中有重要应用。自动纤维束分割技术可以是无监督的或有监督的。对无监督方法（如聚类）的一个常见批评是，无法保证获得具有解剖学意义的纤维束。在这项工作中，我们专注于有监督的纤维束分割，它由来自解剖图谱或示例（即不同受试者的分割纤维束）的先验知识驱动。我们提出了一种有监督的纤维束分割方法，该方法使用多个示例作为先验信息，对新受试者纤维束图中给定的感兴趣纤维束进行分割。我们提出的纤维束分割方法基于流线对应思想，即在不同纤维束图中找到对应流线。在文献中，流线对应问题是用最近邻（NN）策略解决的。不同的是，这里我们将流线对应问题表述为线性分配问题（LAP），这是组合优化的基石。相对于NN，LAP引入了流线之间一对一对应的约束，这迫使对应关系遵循示例与目标纤维束之间被NN忽略的局部解剖差异。在所提出的解决方案中，我们将用于解决LAP的琼克 - 沃尔根南特算法（LAPJV）与一种高效计算流线最近邻的方法相结合，这大大减少了分割纤维束所需的总计算量。此外，我们提出了一种排序策略来合并来自不同示例的对应关系。我们在由人类连接组计划（HCP）数据集生成的纤维束图上验证了所提出的方法，并将分割结果与NN方法和基于感兴趣区域（ROI）的方法进行比较。结果表明，基于LAP的分割比基于ROI的分割要准确得多，并且比NN策略准确得多。我们提供了所提出方法的免费/开源实现。
