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FocAn: automated 3D analysis of DNA repair foci in image stacks acquired by confocal fluorescence microscopy

机译:FocAn:通过共聚焦荧光显微镜获得的图像堆栈中DNA修复灶的自动3D分析

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摘要

Flow chart demonstrating the main steps of the FocAn algorithm consisting of two independent components for nuclei ( ) and foci ( ) identification. In the first step (A), the raw image is normalized. Nucleus identification is then performed using a mean auto local threshold (ALT, B) followed by Gaussian blurring ( ). Together, these steps result in gradual signal separation of nuclear and cytosolic areas ( ), which is also illustrated in ( ). The green, red and blue lines in ( ) represent the intensity profiles of the corresponding colors in ( ), ( ) and ( ), respectively. After that, mid-gray ALT creates a binary image, shown in ( ). This is followed by watershed transformation for separation of overlapping nuclei, encircled with red lines in ( ). The foci identification process starts with Gaussian blurring of the normalized images followed by median ALT ( ). A 3D watershed transformation can be performed optionally, before finally the foci numbers per nucleus are determined ( )
机译:流程图展示了FocAn算法的主要步骤,该算法由两个独立的组件组成,用于核()和焦点()识别。在第一步(A)中,原始图像被标准化。然后使用平均自动局部阈值(ALT,B)和高斯模糊()进行核识别。这些步骤加在一起,导致核和胞质区域逐渐分离信号(),这也显示在()中。 ()中的绿色,红色和蓝色线分别代表(),()和()中相应颜色的强度曲线。之后,中间灰色的ALT创建一个二进制图像,如()中所示。随后是分水岭变换,用于分离重叠的核,并用()中的红线包围。病灶识别过程始于对标准化图像进行高斯模糊处理,然后是中值ALT()。在最终确定每个核的焦点数之前,可以选择执行3D分水岭变换()

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