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Segmenting nuclei in brightfield images with neural networks

Dmytro Fishman, Sten-Oliver Salumaa, Daniel Majoral, Samantha Peel, Jan Wildenhain, Alexander Schreiner, Kaupo Palo, Leopold Parts
doi: https://doi.org/10.1101/764894
Dmytro Fishman
1Department of Computer Science, University of Tartu, J. Liivi 2, 50409, Estonia
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Sten-Oliver Salumaa
1Department of Computer Science, University of Tartu, J. Liivi 2, 50409, Estonia
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Daniel Majoral
1Department of Computer Science, University of Tartu, J. Liivi 2, 50409, Estonia
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Samantha Peel
2Discovery Sciences, R&D, AstraZeneca, Cambridge, United Kingdom
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Jan Wildenhain
2Discovery Sciences, R&D, AstraZeneca, Cambridge, United Kingdom
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Alexander Schreiner
3PerkinElmer Cellular Technologies Germany GmbH, Hamburg, Germany
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Kaupo Palo
3PerkinElmer Cellular Technologies Germany GmbH, Hamburg, Germany
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Leopold Parts
1Department of Computer Science, University of Tartu, J. Liivi 2, 50409, Estonia
4Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SA, United Kingdom
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  • For correspondence: leopold.parts@ut.ee
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Abstract

Identifying nuclei is a standard first step to analysing cells in microscopy images. The traditional approach relies on signal from a DNA stain, or fluorescent transgene expression localised to the nucleus. However, imaging techniques that do not use fluorescence can also carry useful information. Here, we demonstrate that it is possible to accurately segment nuclei directly from brightfield images using deep learning. We confirmed that three convolutional neural network architectures can be adapted for this task, with U-Net achieving the best overall performance, Mask R-CNN providing an additional benefit of instance segmentation, and DeepCell proving too slow for practical application. We found that accurate segmentation is possible using as few as 16 training images and that models trained on images from similar cell lines can extrapolate well. Acquiring data from multiple focal planes further helps distinguish nuclei in the samples. Overall, our work liberates a fluorescence channel reserved for nuclear staining, thus providing more information from the specimen, and reducing reagents and time required for preparing imaging experiments.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted September 10, 2019.
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Segmenting nuclei in brightfield images with neural networks
Dmytro Fishman, Sten-Oliver Salumaa, Daniel Majoral, Samantha Peel, Jan Wildenhain, Alexander Schreiner, Kaupo Palo, Leopold Parts
bioRxiv 764894; doi: https://doi.org/10.1101/764894
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Segmenting nuclei in brightfield images with neural networks
Dmytro Fishman, Sten-Oliver Salumaa, Daniel Majoral, Samantha Peel, Jan Wildenhain, Alexander Schreiner, Kaupo Palo, Leopold Parts
bioRxiv 764894; doi: https://doi.org/10.1101/764894

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