Xu Ting, Vavylonis Dimitrios, Tsai Feng-Ching, Koenderink Gijsje H, Nie Wei, Yusuf Eddy, Wu Jian-Qiu, Huang Xiaolei
Department of Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania, USA.
Department of Physics, Lehigh University, Bethlehem, Pennsylvania, USA.
Sci Rep. 2015 Mar 13;5:9081. doi: 10.1038/srep09081.
Filamentous biopolymer networks in cells and tissues are routinely imaged by confocal microscopy. Image analysis methods enable quantitative study of the properties of these curvilinear networks. However, software tools to quantify the geometry and topology of these often dense 3D networks and to localize network junctions are scarce. To fill this gap, we developed a new software tool called "SOAX", which can accurately extract the centerlines of 3D biopolymer networks and identify network junctions using Stretching Open Active Contours (SOACs). It provides an open-source, user-friendly platform for network centerline extraction, 2D/3D visualization, manual editing and quantitative analysis. We propose a method to quantify the performance of SOAX, which helps determine the optimal extraction parameter values. We quantify several different types of biopolymer networks to demonstrate SOAX's potential to help answer key questions in cell biology and biophysics from a quantitative viewpoint.
细胞和组织中的丝状生物聚合物网络通常通过共聚焦显微镜进行成像。图像分析方法能够对这些曲线网络的特性进行定量研究。然而，用于量化这些通常密集的三维网络的几何形状和拓扑结构以及定位网络连接点的软件工具却很稀缺。为了填补这一空白，我们开发了一种名为“SOAX”的新软件工具，它可以使用拉伸开放活动轮廓（SOACs）准确提取三维生物聚合物网络的中心线并识别网络连接点。它为网络中心线提取、二维/三维可视化、手动编辑和定量分析提供了一个开源、用户友好的平台。我们提出了一种量化SOAX性能的方法，这有助于确定最佳提取参数值。我们对几种不同类型的生物聚合物网络进行了量化，以从定量角度证明SOAX有助于回答细胞生物学和生物物理学中关键问题的潜力。
