Assessing microscope image focus quality with deep learning Article Swipe

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Samuel Yang , Marc Berndl , D. Michael Ando , Mariya Barch , Arunachalam Narayanaswamy , Eric Christiansen , Stephan Hoyer , C. Roat , Jane Hung , Curtis Rueden , Asim Shankar , Steven Finkbeiner , Philip Nelson ·

YOU? · · 2018 · Open Access · · DOI: https://doi.org/10.1186/s12859-018-2087-4

Our deep neural network enables classification of out-of-focus microscope images with both higher accuracy and greater precision than previous approaches via interpretable patch-level focus and certainty predictions. The use of synthetically defocused images precludes the need for a manually annotated training dataset. The model also generalizes to different image and cell types. The framework for model training and image prediction is available as a free software library and the pre-trained model is available for immediate use in Fiji (ImageJ) and CellProfiler.

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Artificial intelligence Focus (optics) Computer science Image quality Computer vision Autofocus Pixel Pattern recognition (psychology) Deep learning Artificial neural network Binary image Task (project management) Image processing Image (mathematics) Optics Physics Management Economics

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Type: article
Language: en
Landing Page: https://doi.org/10.1186/s12859-018-2087-4
PDF: https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-018-2087-4
OA Status: gold
Cited By: 151
References: 24
Related Works: 10
OpenAlex ID: https://openalex.org/W2795106634

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