期刊
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
卷 19, 期 -, 页码 2106-2120出版社
ELSEVIER
DOI: 10.1016/j.csbj.2021.04.019
关键词
Cytoskeleton; Deep learning; Image processing; Actin filaments; Microtubules; Intermediate filaments; Curvilinear objects; Physcomitrella; Plastoskeleton
资金
- Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy [EXC-2189, EXC-2193]
Cytoskeletal filaments play crucial roles in biological cells and organisms, and understanding their geometric and topological organization is key for revealing their functions. High-resolution microscopy and sophisticated image processing software are required for accurate segmentation. Recent advancements in deep learning have started to simplify this task.
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 nontrivial 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. (C) 2021 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据