细胞骨架纤丝是对生物细胞和生物体最重要的结构,因为它们具有多功能性和重要功能。这些生物聚合物通常被组织成具有复杂形态的网状支架。了解这些网络的几何和拓扑组织为其功能角色提供了关键见解。然而,这个不平凡的任务需要高分辨率显微镜和复杂的图像处理/分析软件的组合。对网络结构和连通性的正确分析需要显微图像的精确分割。虽然丝状物体的分割是生物医学成像中一个经过充分研究的概念,在那里追踪神经元和血管是常规的,有相对较少的研究集中在细胞骨架丝和网络从显微图像的分割。显微镜领域的发展,计算机视觉和深度学习,然而,开始促进任务,这反映在最近关于这个话题的文献的增加上。这里,我们的目标是提供关于(半)自动增强的研究的简短总结,分割和跟踪方法是专门为细胞骨架网络的显微图像设计和开发的。除了提供常规方法的概述外,我们报道了最近推出的,深度学习辅助方法以及它们提供的优于经典方法的优势。
Cytoskeletal filaments are structures of utmost importance to biological cells and organisms due to their versatility and the significant functions they perform. These biopolymers are most often organised into network-like scaffolds with a complex morphology. Understanding the geometrical and topological organisation of these networks provides key insights into their functional roles. However, this non-trivial task requires a combination of high-resolution microscopy and sophisticated image processing/analysis software. The correct analysis of the network structure and connectivity needs precise segmentation of microscopic images. While segmentation of filament-like objects is a well-studied concept in biomedical imaging, where tracing of neurons and blood vessels is routine, there are comparatively fewer studies focusing on the segmentation of cytoskeletal filaments and networks from microscopic images. The developments in the fields of microscopy, computer vision and deep learning, however, began to facilitate the task, as reflected by an increase in the recent literature on the topic. Here, we aim to provide a short summary of the research on the (semi-)automated enhancement, segmentation and tracing methods that are particularly designed and developed for microscopic images of cytoskeletal networks. In addition to providing an overview of the conventional methods, we cover the recently introduced, deep-learning-assisted methods alongside the advantages they offer over classical methods.