Machine learning segmentation tool trained on synthetic data for tracking cytoskeleton polymerisation and depolymerisation

Abstract

The cytoskeleton is important in controlling the growth and morphology of plant cells, so tracking its morphological changes is essential. Here, we develop a new machine learning based segmentation tool for microtubules (MTs), which can distinguish between polymerised and depolymerised fibres. To circumvent the low abundance of data, we trained on synthetic images of microtubules from a computational micro-tubule model, pre-processed to reproduce microscope effects and partial depolymerisation. We used this tool to investigate how the MT network in an Arabidopsis thaliana root hair cell repolymerises after depolymerisation under Oryzalin (OZ) drug treatments. Specifically, we show the network initially repolymerises from the shank region. This work demonstrates the viability of using synthetic data to train machine learning systems handling cytoskeletal image data.

Plant phenotyping relevance

植物細胞の微小管画像から重合・脱重合状態を抽出する機械学習セグメンテーション手法の開発が中心であり、植物細胞の形態・状態の表現型計測に該当する。

Code and data availability

The paper states its analysis code is available at a Sainsbury Laboratory GitLab repository (https://gitlab.developers.cam.ac.uk/slcu/teamhj/publications/elangovan_etal_2025), but that URL is not among the allowed_urls, so it cannot be cited. The only allowed repository URLs (Tubulaton, CorticalSim, Cytosim) are cited,

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