School of Information Technologies, University of Sydney, Sydney, NSW, Australia.
Allen Institute for Brain Science, Seattle, WA, USA.
Neuroinformatics. 2018 Apr;16(2):153-166. doi: 10.1007/s12021-017-9353-x.
The automatic neuron reconstruction is important since it accelerates the collection of 3D neuron models for the neuronal morphological studies. The majority of the previous neuron reconstruction methods only focused on tracing neuron fibres without considering the somatic surface. Thus, topological errors often present around the soma area in the results obtained by these tracing methods. Segmentation of the soma structures can be embedded in the existing neuron tracing methods to reduce such topological errors. In this paper, we present a novel method to segment the soma structures with complex geometry. It can be applied along with the existing methods in a fully automated pipeline. An approximate bounding block is firstly estimated based on a geodesic distance transform. Then the soma segmentation is obtained by evolving the surface with a set of morphological operators inside the initial bounding region. By evaluating the methods against the challenging images released by the BigNeuron project, we showed that the proposed method can outperform the existing soma segmentation methods regarding the accuracy. We also showed that the soma segmentation can be used for enhancing the results of existing neuron tracing methods.
自动神经元重建很重要，因为它可以加速收集用于神经元形态研究的 3D 神经元模型。以前的大多数神经元重建方法仅侧重于追踪神经元纤维，而不考虑胞体表面。因此，在这些追踪方法得到的结果中，在胞体区域周围经常出现拓扑错误。可以在现有的神经元追踪方法中嵌入胞体结构的分割，以减少这种拓扑错误。在本文中，我们提出了一种新的方法来分割具有复杂几何形状的胞体结构。它可以与现有的全自动流水线方法一起应用。首先基于测地距离变换估计近似的包围块。然后通过在初始包围区域内使用一组形态学算子来演化表面，从而获得胞体分割。通过在 BigNeuron 项目发布的具有挑战性的图像上评估这些方法，我们表明，与现有的胞体分割方法相比，所提出的方法在准确性方面表现更好。我们还表明，胞体分割可用于增强现有的神经元追踪方法的结果。
