ArticleSynthetic biology (Oxford, England)2023
Automated cell segmentation for reproducibility in bioimage analysis.
Article in Synthetic biology (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
- AIstain: Enhancing microglial phagocytosis analysis through deep learning.Cell reports methods · 2025Article
- Leveraging pre-trained machine learning models for islet quantification in type 1 diabetes.Journal of pathology informatics · 2025Article
- Special issue: reproducibility in synthetic biology.Synthetic biology (Oxford, England) · 2023Article
- Advancing reproducibility can ease the 'hard truths' of synthetic biology.Synthetic biology (Oxford, England) · 2023Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Live-cell imaging is extremely common in synthetic biology research, but its ability to be applied reproducibly across laboratories can be hindered by a lack of standardized image analysis. Here, we introduce a novel cell segmentation method developed as part of a broader Independent Verification & Validation (IV&V) program aimed at characterizing engineered
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.