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Automated 3D segmentation of guard cells enables volumetric analysis of stomatal biomechanics
Automating the three-dimensional (3D) segmentation of stomatal guard cells and other confocal microscopy data is extremely challenging due to hardware limitations, hard-to-localize regions, and limited optical resolution. We present a memory-efficient, attention-based, one-stage segmentation neural...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9782259/ https://www.ncbi.nlm.nih.gov/pubmed/36569557 http://dx.doi.org/10.1016/j.patter.2022.100627 |
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author | Davaasuren, Dolzodmaa Chen, Yintong Jaafar, Leila Marshall, Rayna Dunham, Angelica L. Anderson, Charles T. Wang, James Z. |
author_facet | Davaasuren, Dolzodmaa Chen, Yintong Jaafar, Leila Marshall, Rayna Dunham, Angelica L. Anderson, Charles T. Wang, James Z. |
author_sort | Davaasuren, Dolzodmaa |
collection | PubMed |
description | Automating the three-dimensional (3D) segmentation of stomatal guard cells and other confocal microscopy data is extremely challenging due to hardware limitations, hard-to-localize regions, and limited optical resolution. We present a memory-efficient, attention-based, one-stage segmentation neural network for 3D images of stomatal guard cells. Our model is trained end to end and achieved expert-level accuracy while leveraging only eight human-labeled volume images. As a proof of concept, we applied our model to 3D confocal data from a cell ablation experiment that tests the “polar stiffening” model of stomatal biomechanics. The resulting data allow us to refine this polar stiffening model. This work presents a comprehensive, automated, computer-based volumetric analysis of fluorescent guard cell images. We anticipate that our model will allow biologists to rapidly test cell mechanics and dynamics and help them identify plants that more efficiently use water, a major limiting factor in global agricultural production and an area of critical concern during climate change. |
format | Online Article Text |
id | pubmed-9782259 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-97822592022-12-24 Automated 3D segmentation of guard cells enables volumetric analysis of stomatal biomechanics Davaasuren, Dolzodmaa Chen, Yintong Jaafar, Leila Marshall, Rayna Dunham, Angelica L. Anderson, Charles T. Wang, James Z. Patterns (N Y) Article Automating the three-dimensional (3D) segmentation of stomatal guard cells and other confocal microscopy data is extremely challenging due to hardware limitations, hard-to-localize regions, and limited optical resolution. We present a memory-efficient, attention-based, one-stage segmentation neural network for 3D images of stomatal guard cells. Our model is trained end to end and achieved expert-level accuracy while leveraging only eight human-labeled volume images. As a proof of concept, we applied our model to 3D confocal data from a cell ablation experiment that tests the “polar stiffening” model of stomatal biomechanics. The resulting data allow us to refine this polar stiffening model. This work presents a comprehensive, automated, computer-based volumetric analysis of fluorescent guard cell images. We anticipate that our model will allow biologists to rapidly test cell mechanics and dynamics and help them identify plants that more efficiently use water, a major limiting factor in global agricultural production and an area of critical concern during climate change. Elsevier 2022-11-09 /pmc/articles/PMC9782259/ /pubmed/36569557 http://dx.doi.org/10.1016/j.patter.2022.100627 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Davaasuren, Dolzodmaa Chen, Yintong Jaafar, Leila Marshall, Rayna Dunham, Angelica L. Anderson, Charles T. Wang, James Z. Automated 3D segmentation of guard cells enables volumetric analysis of stomatal biomechanics |
title | Automated 3D segmentation of guard cells enables volumetric analysis of stomatal biomechanics |
title_full | Automated 3D segmentation of guard cells enables volumetric analysis of stomatal biomechanics |
title_fullStr | Automated 3D segmentation of guard cells enables volumetric analysis of stomatal biomechanics |
title_full_unstemmed | Automated 3D segmentation of guard cells enables volumetric analysis of stomatal biomechanics |
title_short | Automated 3D segmentation of guard cells enables volumetric analysis of stomatal biomechanics |
title_sort | automated 3d segmentation of guard cells enables volumetric analysis of stomatal biomechanics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9782259/ https://www.ncbi.nlm.nih.gov/pubmed/36569557 http://dx.doi.org/10.1016/j.patter.2022.100627 |
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