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DeepMIB: User-friendly and open-source software for training of deep learning network for biological image segmentation

View ORCID ProfileIlya Belevich, View ORCID ProfileEija Jokitalo
doi: https://doi.org/10.1101/2020.07.13.200105
Ilya Belevich
Electron Microscopy Unit, Institute of Biotechnology, University of Helsinki, PO Box 56, FI-00014 Helsinki, Finland
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  • For correspondence: ilya.belevich@helsinki.fi eija.jokitalo@helsinki.fi
Eija Jokitalo
Electron Microscopy Unit, Institute of Biotechnology, University of Helsinki, PO Box 56, FI-00014 Helsinki, Finland
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  • For correspondence: ilya.belevich@helsinki.fi eija.jokitalo@helsinki.fi
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Abstract

Deep learning approaches are highly sought after solutions for coping with large amounts of collected datasets and are expected to become an essential part of imaging workflows. However, in most cases, deep learning is still considered as a complex task that only image analysis experts can master. DeepMIB addresses this problem and provides the community with a user-friendly and open-source tool to train convolutional neural networks and apply them to segment 2D and 3D light and electron microscopy datasets.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • http://mib.helsinki.fi/downloads.html

  • http://mib.helsinki.fi/tutorials/deepmib/Figure1aFiles.zip

  • http://mib.helsinki.fi/tutorials/deepmib/Figure1bFiles.zip

  • http://mib.helsinki.fi/tutorials/deepmib/Figure1cFiles.zip

  • http://mib.helsinki.fi/tutorials/deepmib/Figure1dFiles.zip

Copyright 
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-ND 4.0 International license.
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Posted July 14, 2020.
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DeepMIB: User-friendly and open-source software for training of deep learning network for biological image segmentation
Ilya Belevich, Eija Jokitalo
bioRxiv 2020.07.13.200105; doi: https://doi.org/10.1101/2020.07.13.200105
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DeepMIB: User-friendly and open-source software for training of deep learning network for biological image segmentation
Ilya Belevich, Eija Jokitalo
bioRxiv 2020.07.13.200105; doi: https://doi.org/10.1101/2020.07.13.200105

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