Tree ring segmentation using UNEt TRansformer neural network on stained microsections for quantitative wood anatomy

Forests are critical in the terrestrial carbon cycle, and the knowledge of their response to ongoing climate change will be crucial for determining future carbon fluxes and climate trajectories. In areas with contrasting seasons, trees form discrete annual rings that can be assigned to calendar year...

ver descrição completa

Detalhes bibliográficos
Autores: García Hidalgo, Miguel, García Pedrero, Ángel, Rozas Ortiz, Vicente Fernando, Sangüesa Barreda, Gabriel, García Cervigón, Ana I., Resente, Giulia, Wilmking, Martin, Olano Mendoza, José Miguel
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Recursos:Universidad de Valladolid
Repositorio:UVaDOC. Repositorio Documental de la Universidad de Valladolid
OAI Identifier:oai:uvadoc.uva.es:10324/67912
Acesso em linha:https://doi.org/10.3389/fpls.2023.1327163
https://uvadoc.uva.es/handle/10324/67912
Access Level:acceso abierto
Palavra-chave:Image segmentation
Neural network
Quantitative wood anatomy
Tree ring
UNETR
Xylem
id ES_434f157ff7fd143c0903e71053dd4e94
oai_identifier_str oai:uvadoc.uva.es:10324/67912
network_acronym_str ES
network_name_str España
repository_id_str
spelling Tree ring segmentation using UNEt TRansformer neural network on stained microsections for quantitative wood anatomyGarcía Hidalgo, MiguelGarcía Pedrero, ÁngelRozas Ortiz, Vicente FernandoSangüesa Barreda, GabrielGarcía Cervigón, Ana I.Resente, GiuliaWilmking, MartinOlano Mendoza, José MiguelImage segmentationNeural networkQuantitative wood anatomyTree ringUNETRXylemForests are critical in the terrestrial carbon cycle, and the knowledge of their response to ongoing climate change will be crucial for determining future carbon fluxes and climate trajectories. In areas with contrasting seasons, trees form discrete annual rings that can be assigned to calendar years, allowing to extract valuable information about how trees respond to the environment. The anatomical structure of wood provides highly-resolved information about the reaction and adaptation of trees to climate. Quantitative wood anatomy helps to retrieve this information by measuring wood at the cellular level using high-resolution images of wood micro-sections. However, whereas large advances have been made in identifying cellular structures, obtaining meaningful cellular information is still hampered by the correct annual tree ring delimitation on the images. This is a time-consuming task that requires experienced operators to manually delimit ring boundaries. Classic methods of automatic segmentation based on pixel values are being replaced by new approaches using neural networks which are capable of distinguishing structures, even when demarcations require a high level of expertise. Although neural networks have been used for tree ring segmentation on macroscopic images of wood, the complexity of cell patterns in stained microsections of broadleaved species requires adaptive models to accurately accomplish this task. We present an automatic tree ring boundary delineation using neural networks on stained cross-sectional microsection images from beech cores. We trained a UNETR, a combined neural network of UNET and the attention mechanisms of Visual Transformers, to automatically segment annual ring boundaries. Its accuracy was evaluated considering discrepancies with manual segmentation and the consequences of disparity for the goals of quantitative wood anatomy analyses. In most cases (91.8%), automatic segmentation matched or improved manual segmentation, and the rate of vessels assignment to annual rings was similar between the two categories, even when manual segmentation was considered better. The application of convolutional neural networks-based models outperforms human operator segmentations when confronting ring boundary delimitation using specific parameters for quantitative wood anatomy analysis. Current advances on segmentation models may reduce the cost of massive and accurate data collection for quantitative wood anatomy.2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://doi.org/10.3389/fpls.2023.1327163https://uvadoc.uva.es/handle/10324/67912reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolidinstname:Universidad de ValladolidEspañolinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/oai:uvadoc.uva.es:10324/679122026-06-13T12:44:47Z
dc.title.none.fl_str_mv Tree ring segmentation using UNEt TRansformer neural network on stained microsections for quantitative wood anatomy
title Tree ring segmentation using UNEt TRansformer neural network on stained microsections for quantitative wood anatomy
spellingShingle Tree ring segmentation using UNEt TRansformer neural network on stained microsections for quantitative wood anatomy
García Hidalgo, Miguel
Image segmentation
Neural network
Quantitative wood anatomy
Tree ring
UNETR
Xylem
title_short Tree ring segmentation using UNEt TRansformer neural network on stained microsections for quantitative wood anatomy
title_full Tree ring segmentation using UNEt TRansformer neural network on stained microsections for quantitative wood anatomy
title_fullStr Tree ring segmentation using UNEt TRansformer neural network on stained microsections for quantitative wood anatomy
title_full_unstemmed Tree ring segmentation using UNEt TRansformer neural network on stained microsections for quantitative wood anatomy
title_sort Tree ring segmentation using UNEt TRansformer neural network on stained microsections for quantitative wood anatomy
dc.creator.none.fl_str_mv García Hidalgo, Miguel
García Pedrero, Ángel
Rozas Ortiz, Vicente Fernando
Sangüesa Barreda, Gabriel
García Cervigón, Ana I.
Resente, Giulia
Wilmking, Martin
Olano Mendoza, José Miguel
author García Hidalgo, Miguel
author_facet García Hidalgo, Miguel
García Pedrero, Ángel
Rozas Ortiz, Vicente Fernando
Sangüesa Barreda, Gabriel
García Cervigón, Ana I.
Resente, Giulia
Wilmking, Martin
Olano Mendoza, José Miguel
author_role author
author2 García Pedrero, Ángel
Rozas Ortiz, Vicente Fernando
Sangüesa Barreda, Gabriel
García Cervigón, Ana I.
Resente, Giulia
Wilmking, Martin
Olano Mendoza, José Miguel
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Image segmentation
Neural network
Quantitative wood anatomy
Tree ring
UNETR
Xylem
topic Image segmentation
Neural network
Quantitative wood anatomy
Tree ring
UNETR
Xylem
description Forests are critical in the terrestrial carbon cycle, and the knowledge of their response to ongoing climate change will be crucial for determining future carbon fluxes and climate trajectories. In areas with contrasting seasons, trees form discrete annual rings that can be assigned to calendar years, allowing to extract valuable information about how trees respond to the environment. The anatomical structure of wood provides highly-resolved information about the reaction and adaptation of trees to climate. Quantitative wood anatomy helps to retrieve this information by measuring wood at the cellular level using high-resolution images of wood micro-sections. However, whereas large advances have been made in identifying cellular structures, obtaining meaningful cellular information is still hampered by the correct annual tree ring delimitation on the images. This is a time-consuming task that requires experienced operators to manually delimit ring boundaries. Classic methods of automatic segmentation based on pixel values are being replaced by new approaches using neural networks which are capable of distinguishing structures, even when demarcations require a high level of expertise. Although neural networks have been used for tree ring segmentation on macroscopic images of wood, the complexity of cell patterns in stained microsections of broadleaved species requires adaptive models to accurately accomplish this task. We present an automatic tree ring boundary delineation using neural networks on stained cross-sectional microsection images from beech cores. We trained a UNETR, a combined neural network of UNET and the attention mechanisms of Visual Transformers, to automatically segment annual ring boundaries. Its accuracy was evaluated considering discrepancies with manual segmentation and the consequences of disparity for the goals of quantitative wood anatomy analyses. In most cases (91.8%), automatic segmentation matched or improved manual segmentation, and the rate of vessels assignment to annual rings was similar between the two categories, even when manual segmentation was considered better. The application of convolutional neural networks-based models outperforms human operator segmentations when confronting ring boundary delimitation using specific parameters for quantitative wood anatomy analysis. Current advances on segmentation models may reduce the cost of massive and accurate data collection for quantitative wood anatomy.
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.3389/fpls.2023.1327163
https://uvadoc.uva.es/handle/10324/67912
url https://doi.org/10.3389/fpls.2023.1327163
https://uvadoc.uva.es/handle/10324/67912
dc.language.none.fl_str_mv Español
language_invalid_str_mv Español
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolid
instname:Universidad de Valladolid
instname_str Universidad de Valladolid
reponame_str UVaDOC. Repositorio Documental de la Universidad de Valladolid
collection UVaDOC. Repositorio Documental de la Universidad de Valladolid
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869407014784860160
score 15,300724

Registos relacionados