Expanding plant cell microscopy through artificial intelligence focusing on segmentation and virtual staining

Abstract

Fluorescence imaging has become a central tool in plant cell biology, enabling detailed analysis of cellular structures and dynamics. However, challenges such as phototoxicity, photobleaching, and the invasiveness of fluorescent labeling have driven the development of artificial intelligence (AI)-based alternatives. Among these, deep learning-based segmentation and virtual staining have shown significant promise for advancing plant cell microscopy. Compared with traditional methods reliant on manual operations or simple thresholding algorithms, segmentation powered by AI-based image transformation offers enhanced accuracy and reproducibility in quantifying cellular features. Moreover, virtual staining transforms bright-field images into synthetic fluorescence images, enabling non-invasive, high-resolution analyses while bypassing the need for physical labeling. Together, these techniques expand the analytical capabilities of plant cell microscopy, facilitating efficient and precise imaging workflows. Despite their potential, these approaches face technical challenges. Virtual staining relies heavily on high-quality bright-field images and is currently constrained when applied to three-dimensional analyses of complex plant tissues. Future efforts must focus on developing diverse training datasets and advancing AI technologies to overcome these limitations. By offering automated segmentation and virtual staining, AI is transforming plant cell microscopy into a more versatile and powerful tool, paving the way for groundbreaking discoveries and broader applications in plant cell biology.

Plant phenotyping relevance

植物細胞画像におけるAIセグメンテーションと仮想染色を扱うレビューで、細胞特徴の定量化と再現性向上を目的とした画像解析手法が中心である。

Code and data availability

This is a mini-review of AI-based segmentation and virtual staining in plant cell microscopy. It contains no data availability, code deposit, or repository statements; all referenced datasets/models belong to cited prior work (Horiuchi et al. 2025; Ichita et al. 2025), not this paper. The only URLs present are the CC-B

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