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Deep learning automates virus titration from lab images

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a deep learning system to automate plaque counting and virus titration from laboratory images. The system uses two models derived from the Segment Anything Model (SAM) to segment wells and then detect and count plaques within them. Evaluated on various virus datasets and plate formats, the workflow demonstrated strong agreement with manual annotations and expert assessments, promising to reduce manual effort and improve reproducibility in virus analysis. AI

IMPACT Automates a labor-intensive lab process, improving reproducibility and scalability for virology research.

RANK_REASON The cluster contains an academic paper detailing a new deep learning method for a specific scientific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Deep learning automates virus titration from lab images

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The cluster contains an academic paper detailing a new deep learning method for a specific scientific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · José Ignacio Orlando ·

    End-to-end plaque counting and virus titration from laboratory plate images with deep learning

    Plaque assays remain the gold standard readout of virus infectivity; however, plaque counting from plate images is labor-intensive and prone to inter-operator variability. We present an end-to-end, computer-aided workflow for cytopathic effect-based virus titration directly from …

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