ArticleJournal of biomedical optics2026
Artificial-intelligence-driven segmentation and analysis of microbial cells.
Article in Journal of biomedical optics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Artificial Intelligence for Discovery in Life Sciences.Bioconjugate chemistry · 2026Review
- Robust Cell Segmentation for Size Distribution Estimation via Synthetic-Data Training.Biotechnology journal · 2026Article
- A benchmark for environmental microorganism object detection on EMDS-7.Frontiers in microbiology · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Significance: Understanding microbial growth and morphology at the single-cell level is essential for studying microbial physiology, providing valuable insights for research and biotechnology. However, existing workflows often rely on manual cell annotation, which limits efficiency and scalability. Therefore, developing an automated workflow for quantitative analysis of cell growth and morphology is highly desirable. Aim: We aim to develop an AI-driven analysis system that efficiently segments and indexes microbial cells, and quantitatively analyzes individual cellular features without requiring expensive annotations, for automated monitoring of cell counts and morphological characteristics. Approach: The automated system consists of four modular components: denoising, zero-shot segmentation using the Segment Anything Model (SAM), structured post-processing, and a quantitative feature extraction. To evaluate the effectiveness of each component, we conducted ablation experiments and systematically studied their impact on the overall system performance. Results: Denoising and post-processing improved segmentation accuracy by 12.10% and 2.30%, respectively. Among the evaluated SAM variants, the SAM-H model achieved the best performance, with an average error rate of only 3.0% across 1162 manually annotated Conclusions: This framework automates quantitative analysis of microbial cells in high-resolution microscopy images. It will enable advanced research on microbial adaptations, with the potential to accelerate studies of extremophiles under harsh environments.
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Registered trials
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