ReviewComputational and structural biotechnology journal2021
Automated and semi-automated enhancement, segmentation and tracing of cytoskeletal networks in microscopic images: A review.
Review in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 29 citations in OpenAlex.
- A novel attention mechanism for noise-adaptive and robust segmentation of microtubules in microscopy images.BMC bioinformatics · 2026Article
- Synthetic data enables human-grade microtubule analysis with foundation models for segmentation.PLoS computational biology · 2026Article
- GraFT: A robust network-based spatiotemporal analysis of filamentous structures.Science advances · 2026Article
- From Pixels to Patterns: A Multidimensional Framework to Decode Cytoskeletal Organization.Computational and structural biotechnology journal · 2026Review
- From cells to pixels: A decision tree for designing bioimage analysis pipelines.Journal of microscopy · 2025Article
- Deep learning-based cytoskeleton segmentation for accurate high-throughput measurement of cytoskeleton density.Protoplasma · 2025Article
- Differential GTP-dependent in-vitro polymerization of recombinant Physcomitrella FtsZ proteins.Scientific reports · 2025Article
- Data-Driven and Cell-Specific Determination of Nuclei-Associated Actin Structure.Small structures · 2024Article
- Impact of photobleaching on quantitative, spatio-temporal, super-resolution imaging of mitochondria in liveNpj imaging · 2024Article
- Automatic extraction of actin networks in plants.PLoS computational biology · 2023Article
- Data driven and cell specific determination of nuclei-associated actin structure.bioRxiv : the preprint server for biology · 2023Article
- Tracing and tracking filamentous structures across scales: A systematic review.Computational and structural biotechnology journal · 2023Review
- Quantitative mapping of keratin networks in 3D.eLife · 2022Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.