A Robust Actin Filaments Image Analysis Framework
A Robust Actin Filaments Image Analysis Framework
复制标题
DOI：
10.1371/journal.pcbi.1005063
复制
发表时间：
2016-08-01
影响因子：
4.3
通讯作者：
Sahli, Hichem
中科院分区：
文献类型：
--
作者：
Alioscha-Perez, Mitchel;Benadiba, Carine;Sahli, Hichem
细胞骨架是一种高度动态的蛋白质网络，在许多细胞生理过程中起着核心作用，传统上根据其化学组成分为三种组分，即肌动蛋白、微管蛋白和中间丝细胞骨架。了解细胞骨架动力学对于揭示细胞适应任何压力类型的机制至关重要。细胞骨架结构的荧光成像允许分析机械刺激对细胞骨架的影响，但它也在图像处理阶段带来了额外的挑战，例如成像相关伪影的存在和（高通量）自动扫描引入的严重模糊。然而，尽管存在相当数量的基于图像的分析工具来解决图像处理和分析，但它们中的大多数不适合科普上述挑战。图像中的细丝结构可以被认为是准直线段的分段组合（至少在一些更细或更粗的尺度上）。基于这一观察，我们提出了一个三步提取肌动蛋白丝的方法：（i）首先输入图像被分解成一个“卡通”的部分对应的细丝结构的图像，和噪声/纹理的一部分，（ii）在“卡通”的图像，我们应用一个多尺度线检测器耦合（iii）准直丝合并算法提取纤维。所提出的鲁棒肌动蛋白丝图像分析框架允许在存在噪声、伪影和严重模糊的情况下提取单个丝。此外，它还提供了许多参数，如长丝方向，位置和长度，可用于进一步分析。细胞图像分解在生物图像处理中相对未得到充分利用，我们的研究显示了它在解决这些任务时所提供的好处。使用公开的数据集进行实验验证，并在两种不同条件下生长的成骨细胞：静态（对照）和流体剪切应力。所提出的方法表现出更高的灵敏度值和类似的准确性相比，国家的最先进的方法。
The cytoskeleton is a highly dynamical protein network that plays a central role in numerous cellular physiological processes, and is traditionally divided into three components according to its chemical composition, i.e. actin, tubulin and intermediate filament cytoskeletons. Understanding the cytoskeleton dynamics is of prime importance to unveil mechanisms involved in cell adaptation to any stress type. Fluorescence imaging of cytoskeleton structures allows analyzing the impact of mechanical stimulation in the cytoskeleton, but it also imposes additional challenges in the image processing stage, such as the presence of imaging-related artifacts and heavy blurring introduced by (high-throughput) automated scans. However, although there exists a considerable number of image-based analytical tools to address the image processing and analysis, most of them are unfit to cope with the aforementioned challenges. Filamentous structures in images can be considered as a piecewise composition of quasi-straight segments (at least in some finer or coarser scale). Based on this observation, we propose a three-steps actin filaments extraction methodology: (i) first the input image is decomposed into a 'cartoon' part corresponding to the filament structures in the image, and a noise/texture part, (ii) on the 'cartoon' image, we apply a multi-scale line detector coupled with a (iii) quasi-straight filaments merging algorithm for fiber extraction. The proposed robust actin filaments image analysis framework allows extracting individual filaments in the presence of noise, artifacts and heavy blurring. Moreover, it provides numerous parameters such as filaments orientation, position and length, useful for further analysis. Cell image decomposition is relatively under-exploited in biological images processing, and our study shows the benefits it provides when addressing such tasks. Experimental validation was conducted using publicly available datasets, and in osteoblasts grown in two different conditions: static (control) and fluid shear stress. The proposed methodology exhibited higher sensitivity values and similar accuracy compared to state-of-the-art methods.