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

机译:自动定量分析内皮细胞管形成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 R2 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以通过各种体外培养来建模,在这些体外培养中,内皮细胞(EC)形成网络,这些网络被认为可以模拟体内肿瘤的血管形成。基于图像的EC培养模型定量是一种有效表征早期体外血管生成以及前血管生成和抗血管生成试剂作用的有用方法。我们提出了一种图像分析方法来量化二维文化中的EC管形成。该方法通过在傅立叶空间中的高通滤波对图像进行分割,然后进行阈值化和骨架化和修剪过程以生成培养中细胞模式的二进制骨架图像。使用诸如网络熵(NE),节点数,和弦总数,总和平均弦长之类的几个量来量化EC管的演变。相对于手动测量,验证了弦长的自动测量,R2值为0.953,并用于测定作为增加VEGF浓度的函数的输卵管伸展。在文化中的ECs网络样模式上证明了NE,节点数和弦长的测量。

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