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

We present DeepMIB, a new software package that is capable of training convolutional neural networks for segmentation of multidimensional microscopy datasets on any workstation. We demonstrate its successful application for segmentation of 2D and 3D electron and multicolor light microscopy datasets...

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Detalles Bibliográficos
Autores principales: Belevich, Ilya, Jokitalo, Eija
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7954287/
https://www.ncbi.nlm.nih.gov/pubmed/33651804
http://dx.doi.org/10.1371/journal.pcbi.1008374
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author Belevich, Ilya
Jokitalo, Eija
author_facet Belevich, Ilya
Jokitalo, Eija
author_sort Belevich, Ilya
collection PubMed
description We present DeepMIB, a new software package that is capable of training convolutional neural networks for segmentation of multidimensional microscopy datasets on any workstation. We demonstrate its successful application for segmentation of 2D and 3D electron and multicolor light microscopy datasets with isotropic and anisotropic voxels. We distribute DeepMIB as both an open-source multi-platform Matlab code and as compiled standalone application for Windows, MacOS and Linux. It comes in a single package that is simple to install and use as it does not require knowledge of programming. DeepMIB is suitable for everyone interested of bringing a power of deep learning into own image segmentation workflows.
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spelling pubmed-79542872021-03-22 DeepMIB: User-friendly and open-source software for training of deep learning network for biological image segmentation Belevich, Ilya Jokitalo, Eija PLoS Comput Biol Research Article We present DeepMIB, a new software package that is capable of training convolutional neural networks for segmentation of multidimensional microscopy datasets on any workstation. We demonstrate its successful application for segmentation of 2D and 3D electron and multicolor light microscopy datasets with isotropic and anisotropic voxels. We distribute DeepMIB as both an open-source multi-platform Matlab code and as compiled standalone application for Windows, MacOS and Linux. It comes in a single package that is simple to install and use as it does not require knowledge of programming. DeepMIB is suitable for everyone interested of bringing a power of deep learning into own image segmentation workflows. Public Library of Science 2021-03-02 /pmc/articles/PMC7954287/ /pubmed/33651804 http://dx.doi.org/10.1371/journal.pcbi.1008374 Text en © 2021 Belevich, Jokitalo http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Belevich, Ilya
Jokitalo, Eija
DeepMIB: User-friendly and open-source software for training of deep learning network for biological image segmentation
title DeepMIB: User-friendly and open-source software for training of deep learning network for biological image segmentation
title_full DeepMIB: User-friendly and open-source software for training of deep learning network for biological image segmentation
title_fullStr DeepMIB: User-friendly and open-source software for training of deep learning network for biological image segmentation
title_full_unstemmed DeepMIB: User-friendly and open-source software for training of deep learning network for biological image segmentation
title_short DeepMIB: User-friendly and open-source software for training of deep learning network for biological image segmentation
title_sort deepmib: user-friendly and open-source software for training of deep learning network for biological image segmentation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7954287/
https://www.ncbi.nlm.nih.gov/pubmed/33651804
http://dx.doi.org/10.1371/journal.pcbi.1008374
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