AI Insight
This study analyzed 1.7 million microscopy research papers published between 2010 and 2025 to track mentions of 30 bioimage-analysis software packages. The proportion of papers mentioning at least one analysis package increased from 12% in 2010 to 30% in 2025, with usage concentrated on a few general-purpose platforms, particularly ImageJ/Fiji. Deep-learning methods like StarDist and Cellpose are frequently mentioned alongside established platforms, and many papers mention software without formal citation, suggesting citation counts underestimate actual usage.
Why it matters
Different image-analysis programs can produce different measurements from identical images, making software choice critical for research reproducibility. The finding that many researchers fail to cite the software they use highlights a gap in methodological transparency that could hinder other scientists' ability to replicate findings.
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⚠️ Preprint – Noch nicht peer-reviewed
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Most quantitative microscopy measurements pass through image-analysis software, and different programs can return different measurements from the same images. Which programs researchers rely on therefore matters for reproducibility; however, this information lies scattered across the methods sections of the microscopy literature. In this analysis, we show how mentions of 30 software packages changed across 1.7 million full-text microscopy papers published between 2010 and 2025. In 2010, 12% of these papers mentioned at least one of the 22 analysis packages we tracked; by 2025, 30% did. Mentions stayed concentrated on a few general-purpose platforms, led by ImageJ/Fiji. Newer packages are mentioned more often in particular fields, such as QuPath in pathology and napari in electron microscopy. Deep-learning methods are usually mentioned alongside an established platform; papers often run StarDist inside Fiji and Cellpose on its own. Many papers mention a package without citing it, so citation counts understate how often packages appear in published work; authors should make that use visible by citing each package and reporting how they ran it.
Source: Mapping bioimage-analysis software adoption in microscopy