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首页> 外文期刊>Journal of Biomechanics >Cell and tissue deformation measurements: Texture correlation with third-order approximation of displacement gradients
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Cell and tissue deformation measurements: Texture correlation with third-order approximation of displacement gradients

机译：细胞和组织变形测量：与位移梯度三阶近似的纹理相关性

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摘要

Cells remarkably are capable of large deformations during motility and when subjected to mechanical force. Measurement of mechanical deformation (i.e. displacements, strain) is critical to understand functional changes in cells and biological tissues following disease, and to elucidate basic relationships between applied force and cellular biosynthesis. Microscopy-based imaging modalities provide the ability to noninvasively visualize small cell or tissue structures and track their motion over time, often using two-dimensional (2D) digital image (texture) correlation algorithms. For the measurement of complex and nonlinear motion in cells and tissues, implementation of texture correlation algorithms with high order approximations of displacement mapping terms are needed to minimize error. Here, we extend a texture correlation algorithm with up to third-order approximation of displacement mapping terms for the measurement of cell and tissue deformation. We additionally investigate relationships between measurement error and image texture, defined by subset entropy. Displacement measurement error is significantly reduced when the order of displacement mapping terms in the texture correlation algorithm matches or exceeds the order of the deformation observed. Displacement measurement error is also inversely proportional to subset entropy, with well-defined cell and tissue structures leading to high entropy and low error. For cell and tissue studies where complex or nonlinear displacements are expected, texture correlation algorithms with high order terms are required to best characterize the observed deformation.
机译：细胞在运动过程中和受到机械力作用下具有明显的变形能力。测量机械变形（即位移，应变）对于理解疾病后细胞和生物组织的功能变化以及阐明施加力与细胞生物合成之间的基本关系至关重要。基于显微镜的成像模式通常使用二维（2D）数字图像（纹理）相关算法，能够无创地观察小细胞或组织结构并跟踪其随时间的运动。为了测量细胞和组织中的复杂和非线性运动，需要使用位移映射项的高阶近似的纹理相关算法来实现，以最大程度地减少误差。在这里，我们扩展了纹理相关性算法，以位移映射项的三阶近似来测量细胞和组织的变形。我们还研究了由子集熵定义的测量误差与图像纹理之间的关系。当纹理相关算法中的位移映射项的顺序匹配或超过观察到的变形的顺序时，位移测量误差会大大降低。位移测量误差也与子集熵成反比，具有明确定义的细胞和组织结构会导致高熵和低误差。对于需要复杂或非线性位移的细胞和组织研究，需要使用具有高阶项的纹理相关算法来最佳地表征观察到的变形。

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