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:	Giulia Resente, Alexander Gillert, Mario Trouillier, Alba Anadon-Rosell, Richard L. Peters, Georg von Arx, Uwe von Lukas, Martin Wilmking
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	artificial intelligence wood anatomy deep learning lumen area F1 score ROXAS Plant culture SB1-1110
Acceso en línea:	https://doaj.org/article/5dc098263318426dbb165835eb7a1342
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spelling	oai:doaj.org-article:5dc098263318426dbb165835eb7a13422021-11-04T08:36:12ZMask, Train, Repeat! Artificial Intelligence for Quantitative Wood Anatomy1664-462X10.3389/fpls.2021.767400https://doaj.org/article/5dc098263318426dbb165835eb7a13422021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fpls.2021.767400/fullhttps://doaj.org/toc/1664-462XThe 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.Giulia ResenteAlexander GillertMario TrouillierAlba Anadon-RosellAlba Anadon-RosellRichard L. PetersRichard L. PetersGeorg von ArxGeorg von ArxUwe von LukasUwe von LukasMartin WilmkingFrontiers Media S.A.articleartificial intelligencewood anatomydeep learninglumen areaF1 scoreROXASPlant cultureSB1-1110ENFrontiers in Plant Science, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence wood anatomy deep learning lumen area F1 score ROXAS Plant culture SB1-1110
spellingShingle	artificial intelligence wood anatomy deep learning lumen area F1 score ROXAS Plant culture SB1-1110 Giulia Resente Alexander Gillert Mario Trouillier Alba Anadon-Rosell Alba Anadon-Rosell Richard L. Peters Richard L. Peters Georg von Arx Georg von Arx Uwe von Lukas Uwe von Lukas Martin Wilmking Mask, Train, Repeat! Artificial Intelligence for Quantitative Wood Anatomy
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.
format	article
author	Giulia Resente Alexander Gillert Mario Trouillier Alba Anadon-Rosell Alba Anadon-Rosell Richard L. Peters Richard L. Peters Georg von Arx Georg von Arx Uwe von Lukas Uwe von Lukas Martin Wilmking
author_facet	Giulia Resente Alexander Gillert Mario Trouillier Alba Anadon-Rosell Alba Anadon-Rosell Richard L. Peters Richard L. Peters Georg von Arx Georg von Arx Uwe von Lukas Uwe von Lukas Martin Wilmking
author_sort	Giulia Resente
title	Mask, Train, Repeat! Artificial Intelligence for Quantitative Wood Anatomy
title_short	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_sort	mask, train, repeat! artificial intelligence for quantitative wood anatomy
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/5dc098263318426dbb165835eb7a1342
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Mask, Train, Repeat! Artificial Intelligence for Quantitative Wood Anatomy

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