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Accurate and versatile 3D segmentation of plant tissues at cellular resolution
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(2020) Accurate and versatile 3D segmentation of plant tissues at cellular resolution. eLife, 9 . e57613. doi:10.7554/elife.57613 ISSN 2050-084X.
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WRAP-Accurate-versatile-3D-segmentation-plant-tissues-cellular-resolution-Bassel-2020.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (7Mb) | Preview |
Official URL: https://doi.org/10.7554/elife.57613
Abstract
Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge. Here, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells. PlantSeg employs a convolutional neural network to predict cell boundaries and graph partitioning to segment cells based on the neural network predictions. PlantSeg was trained on fixed and live plant organs imaged with confocal and light sheet microscopes. PlantSeg delivers accurate results and generalizes well across different tissues, scales, acquisition settings even on non plant samples. We present results of PlantSeg applications in diverse developmental contexts. PlantSeg is free and open-source, with both a command line and a user-friendly graphical interface.
Item Type: | Journal Article | |||||||||||||||
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Subjects: | Q Science > QH Natural history Q Science > QK Botany T Technology > TA Engineering (General). Civil engineering (General) |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) | |||||||||||||||
SWORD Depositor: | Library Publications Router | |||||||||||||||
Library of Congress Subject Headings (LCSH): | Plant cells and tissues, Cell separation, Three-dimensional imaging , Image processing—Digital techniques, Cell separation -- Simulation methods | |||||||||||||||
Journal or Publication Title: | eLife | |||||||||||||||
Publisher: | eLife Sciences Publications, Ltd | |||||||||||||||
ISSN: | 2050-084X | |||||||||||||||
Official Date: | 29 July 2020 | |||||||||||||||
Dates: |
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Volume: | 9 | |||||||||||||||
Article Number: | e57613 | |||||||||||||||
DOI: | 10.7554/elife.57613 | |||||||||||||||
Status: | Peer Reviewed | |||||||||||||||
Publication Status: | Published | |||||||||||||||
Reuse Statement (publisher, data, author rights): | ** From eLife via Jisc Publications Router ** History: collection 2020; received 06-04-2020; accepted 28-07-2020; pub-electronic 29-07-2020. ** Licence for this article: http://creativecommons.org/licenses/by/4.0/ | |||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||
Copyright Holders: | © 2020, Wolny et al. | |||||||||||||||
Date of first compliant deposit: | 28 October 2020 | |||||||||||||||
Date of first compliant Open Access: | 30 October 2020 | |||||||||||||||
RIOXX Funder/Project Grant: |
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