Department of Neuroscience, Mount Sinai School of Medicine, New York, NY, USA.
J Neurosci Methods. 2009 Oct 30;184(1):169-75. doi: 10.1016/j.jneumeth.2009.07.021. Epub 2009 Jul 24.
Tracing the centerline of the dendritic arbor of neurons is a powerful technique for analyzing neuronal morphology. In the various neuron tracing algorithms in use nowadays, the competing goals of computational efficiency and robustness are generally traded off against each other. We present a novel method for tracing the centerline of a neuron from confocal image stacks, which provides an optimal balance between these objectives. Using only local information, thin cross-sectional layers of voxels ('scoops') are iteratively carved out of the structure, and clustered based on connectivity. Each cluster contributes a node along the centerline, which is created by connecting successive nodes until all object voxels are exhausted. While data segmentation is independent of this algorithm, we illustrate the use of the ISODATA method to achieve dynamic (local) segmentation. Diameter estimation at each node is calculated using the Rayburst Sampling algorithm, and spurious end nodes caused by surface irregularities are then removed. On standard computing hardware the algorithm can process hundreds of thousands of voxels per second, easily handling the multi-gigabyte datasets resulting from high-resolution confocal microscopy imaging of neurons. This method provides an accurate and efficient means for centerline extraction that is suitable for interactive neuron tracing applications.
追踪神经元树突分支的中心线是分析神经元形态的一种有力技术。在目前使用的各种神经元追踪算法中，计算效率和鲁棒性这两个相互竞争的目标通常需要相互折衷。我们提出了一种从共聚焦图像堆栈中追踪神经元中心线的新方法，该方法在这些目标之间提供了最佳平衡。该方法仅使用局部信息，从结构中迭代地雕刻出薄的横截面体素层（“勺”），并根据连通性进行聚类。每个聚类沿着中心线贡献一个节点，该节点通过连接连续的节点来创建，直到耗尽所有对象体素。虽然数据分割与该算法无关，但我们展示了使用 ISODATA 方法来实现动态（局部）分割。使用 Rayburst Sampling 算法在每个节点计算直径估计，然后去除由于表面不规则而产生的虚假末端节点。在标准计算硬件上，该算法每秒可以处理数十万个体素，轻松处理由神经元高分辨率共聚焦显微镜成像产生的数千兆字节数据集。该方法提供了一种准确高效的中心线提取方法，适用于交互式神经元追踪应用。
