A key step toward biologically interpretable analysis of microscopy image-based assays is rigorous quantitative validation with metrics appropriate for the particular application in use. Here we describe this challenge for both classical and modern deep learning-based image analysis approaches and discuss possible solutions for automating and streamlining the validation process in the next five to ten years.
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Acknowledgements
J.C. is supported by the Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung, BMBF) in Germany under funding reference 161L0272 and supported by the Ministry of Culture and Science of the State of North Rhine-Westphalia (Ministerium für Kultur und Wissenschaft des Landes Nordrhein-Westfalen, MKW NRW) . We acknowledge the founder of the Allen Institute for Cell Science, Paul G. Allen, for his vision, encouragement and support.
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J.C., M.P.V. and S.M.R. all contributed to all aspects of this work.
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Chen, J., Viana, M.P. & Rafelski, S.M. When seeing is not believing: application-appropriate validation matters for quantitative bioimage analysis. Nat Methods 20, 968–970 (2023). https://doi.org/10.1038/s41592-023-01881-4
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DOI: https://doi.org/10.1038/s41592-023-01881-4
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