Semi-automated region of interest generation for the analysis of optically recorded neuronal activity.
Semi-automated region of interest generation for the analysis of optically recorded neuronal activity.
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DOI：
10.1016/j.neuroimage.2009.04.016
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发表时间：
2009-10-01
期刊：
影响因子：
5.7
通讯作者：
Tuong CM
中科院分区：
文献类型：
--
作者：
Mellen NM;Tuong CM
微电极应用的膜渗透Ca 2+指示剂提供了单细胞分辨率的网络功能。更广泛和更有效地使用这种技术的一个障碍是难以从所研究的网络的活性成分中提取荧光信号。在这里，我们提出了一种半自动感兴趣区域（ROI）检测方法，该方法利用了这些指标通常产生的体细胞信号的空间紧凑、缓慢时变的特征。首先，对图像系列进行差分以消除静态和非常缓慢变化的荧光值，然后对差分图像系列在空间域中进行低通滤波，以消除亮度的时间孤立波动。然后对该处理后的图像系列进行阈值处理，使得亮度波动的像素区域被设置为白色，而所有其他区域被设置为黑色。对二值图像进行平均，然后进行迭代阈值处理以提取与暗细胞和亮细胞相关联的ROI。然后使用生成的ROI分析原始图像系列，之后最终用户拒绝虚假信号。这些方法被应用到呼吸网络的新生大鼠倾斜矢状板的准备，并模拟与信噪比范围在1.0 - 0.2之间。仿真结果表明，随着噪声的增加，算法性能逐渐下降。由于信号提取是分析时变Ca 2+信号的必要的第一步，因此半自动ROI检测使研究人员可以专注于下一步：从使用这些方法生成的相对完整的集合中选择感兴趣的迹线。
Bath-applied membrane-permeant Ca2+ indicators offer access to network function with single-cell resolution. A barrier to wider and more efficient use of this technique is the difficulty of extracting fluorescence signals from the active constituents of the network under study. Here we present a method for semi-automatic region of interest (ROI) detection that exploits the spatially compact, slowly time-varying character of the somatic signals that these indicators typically produce. First, the image series is differenced to eliminate static and very slowly varying fluorescence values, and then the differenced image series undergoes low-pass filtering in the spatial domain, to eliminate temporally isolated fluctuations in brightness. This processed image series is then thresholded so that pixel regions of fluctuating brightness are set to white, while all other regions are set to black. Binary images are averaged, and then subjected to iterative thresholding to extract ROIs associated with both dim and bright cells. The original image series is then analyzed using the generated ROIs, after which the end-user rejects spurious signals. These methods are applied to respiratory networks in the neonate rat tilted sagittal slab preparation, and to simulations with signal-to-noise ratios ranging between 1.0 – 0.2. Simulations established that algorithm performance degraded gracefully with increasing noise. Because signal extraction is the necessary first step in the analysis of time-varying Ca2+ signals, semi-automated ROI detection frees the researcher to focus on the next step: selecting traces of interest from the relatively complete set generated using these methods.
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