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Mask, Train, Repeat! Artificial Intelligence for Quantitative Wood Anatomy

The recent developments in artificial intelligence have the potential to facilitate new research methods in ecology. Especially Deep Convolutional Neural Networks (DCNNs) have been shown to outperform other approaches in automatic image analyses. Here we apply a DCNN to facilitate quantitative wood...

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Autores principales: Resente, Giulia, Gillert, Alexander, Trouillier, Mario, Anadon-Rosell, Alba, Peters, Richard L., von Arx, Georg, von Lukas, Uwe, Wilmking, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8601631/
https://www.ncbi.nlm.nih.gov/pubmed/34804101
http://dx.doi.org/10.3389/fpls.2021.767400
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author Resente, Giulia
Gillert, Alexander
Trouillier, Mario
Anadon-Rosell, Alba
Peters, Richard L.
von Arx, Georg
von Lukas, Uwe
Wilmking, Martin
author_facet Resente, Giulia
Gillert, Alexander
Trouillier, Mario
Anadon-Rosell, Alba
Peters, Richard L.
von Arx, Georg
von Lukas, Uwe
Wilmking, Martin
author_sort Resente, Giulia
collection PubMed
description The recent developments in artificial intelligence have the potential to facilitate new research methods in ecology. Especially Deep Convolutional Neural Networks (DCNNs) have been shown to outperform other approaches in automatic image analyses. Here we apply a DCNN to facilitate quantitative wood anatomical (QWA) analyses, where the main challenges reside in the detection of a high number of cells, in the intrinsic variability of wood anatomical features, and in the sample quality. To properly classify and interpret features within the images, DCNNs need to undergo a training stage. We performed the training with images from transversal wood anatomical sections, together with manually created optimal outputs of the target cell areas. The target species included an example for the most common wood anatomical structures: four conifer species; a diffuse-porous species, black alder (Alnus glutinosa L.); a diffuse to semi-diffuse-porous species, European beech (Fagus sylvatica L.); and a ring-porous species, sessile oak (Quercus petraea Liebl.). The DCNN was created in Python with Pytorch, and relies on a Mask-RCNN architecture. The developed algorithm detects and segments cells, and provides information on the measurement accuracy. To evaluate the performance of this tool we compared our Mask-RCNN outputs with U-Net, a model architecture employed in a similar study, and with ROXAS, a program based on traditional image analysis techniques. First, we evaluated how many target cells were correctly recognized. Next, we assessed the cell measurement accuracy by evaluating the number of pixels that were correctly assigned to each target cell. Overall, the “learning process” defining artificial intelligence plays a key role in overcoming the issues that are usually manually solved in QWA analyses. Mask-RCNN is the model that better detects which are the features characterizing a target cell when these issues occur. In general, U-Net did not attain the other algorithms’ performance, while ROXAS performed best for conifers, and Mask-RCNN showed the highest accuracy in detecting target cells and segmenting lumen areas of angiosperms. Our research demonstrates that future software tools for QWA analyses would greatly benefit from using DCNNs, saving time during the analysis phase, and providing a flexible approach that allows model retraining.
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spelling pubmed-86016312021-11-19 Mask, Train, Repeat! Artificial Intelligence for Quantitative Wood Anatomy Resente, Giulia Gillert, Alexander Trouillier, Mario Anadon-Rosell, Alba Peters, Richard L. von Arx, Georg von Lukas, Uwe Wilmking, Martin Front Plant Sci Plant Science The recent developments in artificial intelligence have the potential to facilitate new research methods in ecology. Especially Deep Convolutional Neural Networks (DCNNs) have been shown to outperform other approaches in automatic image analyses. Here we apply a DCNN to facilitate quantitative wood anatomical (QWA) analyses, where the main challenges reside in the detection of a high number of cells, in the intrinsic variability of wood anatomical features, and in the sample quality. To properly classify and interpret features within the images, DCNNs need to undergo a training stage. We performed the training with images from transversal wood anatomical sections, together with manually created optimal outputs of the target cell areas. The target species included an example for the most common wood anatomical structures: four conifer species; a diffuse-porous species, black alder (Alnus glutinosa L.); a diffuse to semi-diffuse-porous species, European beech (Fagus sylvatica L.); and a ring-porous species, sessile oak (Quercus petraea Liebl.). The DCNN was created in Python with Pytorch, and relies on a Mask-RCNN architecture. The developed algorithm detects and segments cells, and provides information on the measurement accuracy. To evaluate the performance of this tool we compared our Mask-RCNN outputs with U-Net, a model architecture employed in a similar study, and with ROXAS, a program based on traditional image analysis techniques. First, we evaluated how many target cells were correctly recognized. Next, we assessed the cell measurement accuracy by evaluating the number of pixels that were correctly assigned to each target cell. Overall, the “learning process” defining artificial intelligence plays a key role in overcoming the issues that are usually manually solved in QWA analyses. Mask-RCNN is the model that better detects which are the features characterizing a target cell when these issues occur. In general, U-Net did not attain the other algorithms’ performance, while ROXAS performed best for conifers, and Mask-RCNN showed the highest accuracy in detecting target cells and segmenting lumen areas of angiosperms. Our research demonstrates that future software tools for QWA analyses would greatly benefit from using DCNNs, saving time during the analysis phase, and providing a flexible approach that allows model retraining. Frontiers Media S.A. 2021-11-04 /pmc/articles/PMC8601631/ /pubmed/34804101 http://dx.doi.org/10.3389/fpls.2021.767400 Text en Copyright © 2021 Resente, Gillert, Trouillier, Anadon-Rosell, Peters, von Arx, von Lukas and Wilmking. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Resente, Giulia
Gillert, Alexander
Trouillier, Mario
Anadon-Rosell, Alba
Peters, Richard L.
von Arx, Georg
von Lukas, Uwe
Wilmking, Martin
Mask, Train, Repeat! Artificial Intelligence for Quantitative Wood Anatomy
title Mask, Train, Repeat! Artificial Intelligence for Quantitative Wood Anatomy
title_full Mask, Train, Repeat! Artificial Intelligence for Quantitative Wood Anatomy
title_fullStr Mask, Train, Repeat! Artificial Intelligence for Quantitative Wood Anatomy
title_full_unstemmed Mask, Train, Repeat! Artificial Intelligence for Quantitative Wood Anatomy
title_short Mask, Train, Repeat! Artificial Intelligence for Quantitative Wood Anatomy
title_sort mask, train, repeat! artificial intelligence for quantitative wood anatomy
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8601631/
https://www.ncbi.nlm.nih.gov/pubmed/34804101
http://dx.doi.org/10.3389/fpls.2021.767400
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