快速无偏量化生物图像中纤维特征排列的工作流程。

IF 2.4 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Frontiers in Computer Science Pub Date : 2021-10-01 Epub Date: 2021-10-14 DOI:10.3389/fcomp.2021.745831
Stefania Marcotti, Deandra Belo de Freitas, Lee D Troughton, Fiona N Kenny, Tanya J Shaw, Brian M Stramer, Patrick W Oakes
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引用次数: 0

摘要

目前,测量细胞细胞骨架和周围细胞外基质(ECM)的组织结构受到广泛关注,因为局部和全局排列的变化都能突出细胞功能和细胞外环境材料特性的改变。目前已开发出不同的方法来量化这些结构,这些方法通常基于纤维分割或矩阵表示和图像转换,各有利弊。在这里,我们介绍 AFT-Alignment by Fourier Transform,这是一种利用二维快速傅立叶变换(FFT)量化显微图像中纤维特征排列的工作流程。我们利用已有的细胞和 ECM 图像数据集演示了我们的方法,并将此工作流程与其他两种著名的 ImageJ 算法进行了比较和对比,以量化图像特征对齐情况。这些比较表明,AFT 基于网格的 FFT 方法具有许多优势。1) 灵活定义窗口和邻域大小,允许执行参数搜索,以确定执行配准度量的最佳长度尺度。因此,这种方法很容易适应不同的图像分辨率和生物系统。2) 通过比较邻域大小,可以提取配准衰减的长度尺度,从而揭示特征保持各向异性的总体距离。3) 该方法对信号源不敏感,因此适用于多种成像模式,而且与分割方法相比,依赖的输入参数更少。4) 最后,与分割方法相比,该算法计算成本低廉,在标准台式电脑上不到一秒钟就能评估高分辨率图像。这使得筛选大量实验扰动或检查长长度尺度的大型图像变得可行。为了让更多人能够使用,我们提供了 MATLAB 和 Python 版本,并提供了单幅图像和批量处理的示例数据集。此外,我们还提供了一种方法来自动搜索最佳窗口和邻域大小的参数,以及测量随着长度尺度逐渐增大对齐度的衰减。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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.

Measuring the organisation 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 fibre 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 neighbourhood 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 neighbourhood 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 neighbourhood sizes, as well as to measure the decay in alignment over progressively increasing length scales.

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来源期刊
Frontiers in Computer Science
Frontiers in Computer Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.30
自引率
0.00%
发文量
152
审稿时长
13 weeks
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