A Workflow for Rapid Unbiased Quantification of Fibrillar Feature Alignment in Biological Images
A Workflow for Rapid Unbiased Quantification of Fibrillar Feature Alignment in Biological Images
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DOI：
10.3389/fcomp.2021.745831
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发表时间：
2021-10-14
影响因子：
2.6
通讯作者：
Oakes, Patrick W.
中科院分区：
文献类型：
--
作者：
Marcotti, Stefania;Belo de Freitas, Deandra;Oakes, Patrick W.
测量细胞细胞骨架和周围细胞外基质（ECM）的组织目前受到广泛关注，因为局部和整体排列的变化可以突出细胞功能和细胞外环境材料特性的变化。已经开发了不同的方法来量化这些结构，通常基于纤维分割或基于图像的矩阵表示和变换，每种方法都有自己的优点和缺点。在这里，我们提出了AFT -通过傅立叶变换对齐，一个工作流程，以量化利用二维快速傅立叶变换（FFT）显微图像中的纤维特征的对齐。使用预先存在的细胞和ECM图像数据集，我们展示了我们的方法，并将此工作流程与其他两个著名的ImageJ算法进行比较和对比，以量化图像特征对齐。这些比较表明，AFT由于其基于网格的FFT方法而具有许多优点。1)定义窗口和邻域大小的灵活性允许执行参数搜索以确定最佳长度尺度来执行比对度量。因此，这种方法可以轻松适应不同的图像分辨率和生物系统。2)可以通过比较邻域大小来提取对齐衰减的长度尺度，从而揭示特征保持各向异性的总体距离。3)该方法是矛盾的信号源，从而使其适用于广泛的成像方式，是依赖于更少的输入参数比分割方法。4)最后，与分割方法相比，该算法在计算上是便宜的，因为高分辨率图像可以在不到一秒的时间内在标准台式计算机上进行评估。这使得它能够筛选大量的实验扰动或检查长尺度上的大图像。实现在MATLAB和Python中提供，以提供更广泛的可访问性，并提供用于单个图像和批处理的示例数据集。此外，我们还包括一种方法来自动搜索参数的最佳窗口和邻域大小，以及测量衰减对齐逐渐增加的长度尺度。
Measuring the organization of the cellular cytoskeleton and the surrounding extracellular matrix (ECM) is currently of wide interest as changes in both local and global alignment can highlight alterations in cellular functions and material properties of the extracellular environment. Different approaches have been developed to quantify these structures, typically based on fiber segmentation or on matrix representation and transformation of the image, each with its own advantages and disadvantages. Here we present AFT - Alignment by Fourier Transform, a workflow to quantify the alignment of fibrillar features in microscopy images exploiting 2D Fast Fourier Transforms (FFT). Using pre-existing datasets of cell and ECM images, we demonstrate our approach and compare and contrast this workflow with two other well-known ImageJ algorithms to quantify image feature alignment. These comparisons reveal that AFT has a number of advantages due to its grid-based FFT approach. 1) Flexibility in defining the window and neighborhood sizes allows for performing a parameter search to determine an optimal length scale to carry out alignment metrics. This approach can thus easily accommodate different image resolutions and biological systems. 2) The length scale of decay in alignment can be extracted by comparing neighborhood sizes, revealing the overall distance that features remain anisotropic. 3) The approach is ambivalent to the signal source, thus making it applicable for a wide range of imaging modalities and is dependent on fewer input parameters than segmentation methods. 4) Finally, compared to segmentation methods, this algorithm is computationally inexpensive, as high-resolution images can be evaluated in less than a second on a standard desktop computer. This makes it feasible to screen numerous experimental perturbations or examine large images over long length scales. Implementation is made available in both MATLAB and Python for wider accessibility, with example datasets for single images and batch processing. Additionally, we include an approach to automatically search parameters for optimum window and neighborhood sizes, as well as to measure the decay in alignment over progressively increasing length scales.