LAUSR

Live-cell imaging has enabled the visualization of cytoskeletal dynamics with high spatiotemporal resolution, producing vast, and complex datasets. Recent advancements in live-cell imaging techniques have significantly increased data dimensionality and throughput, challenging conventional qualitative analysis methods. Computational approaches, including machine learning-based image processing, have emerged as powerful tools for extracting quantitative features from these datasets, facilitating systematic analysis of cytoskeletal organization and dynamics. In this review, we outline image analysis techniques for quantification of cytoskeletal structures, focusing on microscopic image transformation and feature extraction. We discuss classical image-processing methods, such as filtering and segmentation, as well as recent applications of deep learning in cytoskeletal analysis. Furthermore, we revisit classical studies on cortical microtubule reorganization after plant cytokinesis, and explore how modern computational techniques can provide new insights into traditional concepts.