Morrill Erica E, Tulepbergenov Azamat N, Stender Christina J, Lamichhane Roshani, Brown Raquel J, Lujan Trevor J
Department of Mechanical and Biomedical Engineering, Boise State University, 1910 University Drive, Boise, ID, 83725-2085, USA.
Department of Computer Science, Boise State University, Boise, ID, 83725-2055, USA.
Biomech Model Mechanobiol. 2016 Dec;15(6):1467-1478. doi: 10.1007/s10237-016-0776-3. Epub 2016 Mar 5.
The mechanical behavior of soft connective tissue is governed by a dense network of fibrillar proteins in the extracellular matrix. Characterization of this fibrous network requires the accurate extraction of descriptive structural parameters from imaging data, including fiber dispersion and mean fiber orientation. Common methods to quantify fiber parameters include fast Fourier transforms (FFT) and structure tensors; however, information is limited on the accuracy of these methods. In this study, we compared these two methods using test images of fiber networks with varying topology. The FFT method with a band-pass filter was the most accurate, with an error of [Formula: see text] in measuring mean fiber orientation and an error of [Formula: see text] in measuring fiber dispersion in the test images. The accuracy of the structure tensor method was approximately five times worse than the FFT band-pass method when measuring fiber dispersion. A free software application, FiberFit, was then developed that utilizes an FFT band-pass filter to fit fiber orientations to a semicircular von Mises distribution. FiberFit was used to measure collagen fibril organization in confocal images of bovine ligament at magnifications of [Formula: see text] and [Formula: see text]. Grayscale conversion prior to FFT analysis gave the most accurate results, with errors of [Formula: see text] for mean fiber orientation and [Formula: see text] for fiber dispersion when measuring confocal images at [Formula: see text]. By developing and validating a software application that facilitates the automated analysis of fiber organization, this study can help advance a mechanistic understanding of collagen networks and help clarify the mechanobiology of soft tissue remodeling and repair.
软结缔组织的力学行为由细胞外基质中密集的纤维状蛋白质网络所支配。对这种纤维网络的表征需要从成像数据中准确提取描述性结构参数，包括纤维分散度和平均纤维取向。量化纤维参数的常用方法包括快速傅里叶变换（FFT）和结构张量；然而，关于这些方法的准确性的信息有限。在本研究中，我们使用具有不同拓扑结构的纤维网络测试图像比较了这两种方法。带通滤波器的FFT方法最为准确，在测试图像中测量平均纤维取向时的误差为[公式：见正文]，测量纤维分散度时的误差为[公式：见正文]。在测量纤维分散度时，结构张量方法的准确性比FFT带通方法差约五倍。然后开发了一个免费软件应用程序FiberFit，它利用FFT带通滤波器将纤维取向拟合为半圆形的冯·米塞斯分布。FiberFit用于在[公式：见正文]和[公式：见正文]放大倍数下测量牛韧带共聚焦图像中的胶原纤维组织。在FFT分析之前进行灰度转换可得到最准确的结果，在[公式：见正文]测量共聚焦图像时，平均纤维取向的误差为[公式：见正文]，纤维分散度的误差为[公式：见正文]。通过开发和验证一个有助于纤维组织自动分析的软件应用程序，本研究有助于推进对胶原网络的力学理解，并有助于阐明软组织重塑和修复的力学生物学。
