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Automatic Image Analysis method for quantification of tubeformation by Endothelial cells in vitro

机译:以体外内皮细胞量化的自动图像分析方法

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Early stages of tumor angiogenesis can be modeled by various in vitro cultures in which endothelial cells (ECs) form networks that are considered to mimic the vascularization of tumors in vivo. Image based quantification of EC culture model is a useful method for effective characterization of early stage in vitro vasculogenesis and the effects of pro and anti-angiogenesis reagents. We propose an image analysis method to quantify the EC tube formation in 2D cultures. The method segments images by high pass filtering in Fourier space, followed by thresholding and a skeletonization and pruning process to generate the binary skeleton image of the cell patterns in culture. Several quantities such as the network entropy (NE), the node number, total number of chords, total and average chord length were used to quantify the evolution of EC tubes. The automatic measurement of chord length was validated against manual measurement, achieving an R~2 value of 0.953, and was used to assay for tubal extension as a function of increasing VEGF concentration. Measurements of NE, node number, chord lengths were .demonstrated on ECs network-like patterns in culture.
机译:肿瘤血管生成的早期阶段可通过各种体外培养物来建模,其中内皮细胞(EC),其被认为在体内肿瘤的模拟物的血管形成网络。 EC培养模型的基于图像的量化是用于在体外血管生成早期的有效表征和亲和抗血管生成试剂的效果的有用方法。我们提出了一种图像分析方法,来量化2D培养物中的EC管形成。通过高通在傅立叶空间滤波的方法段图像,随后通过阈值和骨架和修剪处理,以产生在培养物中的单元图案的二进制图像骨架。几个量,例如网络熵(NE)时,节点数目,和弦的总数,总平均弦长被用来量化EC管的演化。弦长的自动测量进行了验证针对手工测量,实现的0.953的R〜2值,并用于测定输卵管扩展作为增加VEGF浓度的函数。 NE,节点数量的测量,弦长被.demonstrated上的EC网络状在培养物中的图案。

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