Images accompanying the paper "Enabling high-throughput quantitative wood anatomy through a dedicated pipeline"
(2025)
- Author
- Jan Van den Bulcke (UGent) , Ruben De Blaere (UGent) , Louis Verschuren (UGent) and Simon Vansuyt (UGent)
- Organization
- Project
-
- SmartWoodID: Smart classification of Congolese timbers: deep learning techniques for enforcing forest conservation
- ACcurate Temperature REconstructions and climate change mapping in tree rings of Ancient bristlecone pines, the Longest-living trees in the world [ACTREAL]
- A game-changing perspective on intra-seasonal wood formation dynamics using high-resolution X-ray Computed Tomography to elucidate leaf senescence and autumn dynamics of temperate deciduous trees in Europe
- Abstract
- Software for building your own Gigapixel Woodbot can be found on https://doi.org/10.5281/zenodo.14637832. Releases of software for analysis of the images can be found on https://doi.org/10.5281/zenodo.14637855. The training datasets and trained YOLOv8 model needed to run the analysis, can be found on https://doi.org/10.5281/zenodo.14604996. The increment core datasets accompanying these full disk images, can be found on https://doi.org/10.5281/zenodo.14627909. This is part of an entire sample preparation, imaging and analysis pipeline available in the paper "Enabling high-throughput quantitative wood anatomy through a dedicated pipeline" in Plant Methods by Van den Bulcke and co-authors (DOI to be added upon acceptance of publication). Please cite our work when using these data. Also acknowledge the following collections: The five beech discs used as in this paper were collected by the Institute for Nature and Forest Research (INBO: https://ror.org/00j54wy13) as part of the Intensive Monitoring Forest Ecosystems Measurement Network (International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests level II). These discs (Tw81397, Tw81403, Tw81405, Tw81408, Tw81410) are part of the Tervuren xylarium, the wood collection curated at the Royal Museum for Central Africa, Belgium (https://ror.org/001805t51).
- Keywords
- wood anatomy, gigapixel imaging, deep learning
- License
- CC-BY-4.0
- Access
- open access
Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01JKAJJFWTD30ND7T237FE0A3R
@misc{01JKAJJFWTD30ND7T237FE0A3R,
abstract = {{Software for building your own Gigapixel Woodbot can be found on https://doi.org/10.5281/zenodo.14637832.
Releases of software for analysis of the images can be found on https://doi.org/10.5281/zenodo.14637855.
The training datasets and trained YOLOv8 model needed to run the analysis, can be found on https://doi.org/10.5281/zenodo.14604996.
The increment core datasets accompanying these full disk images, can be found on https://doi.org/10.5281/zenodo.14627909.
This is part of an entire sample preparation, imaging and analysis pipeline available in the paper "Enabling high-throughput quantitative wood anatomy through a dedicated pipeline" in Plant Methods by Van den Bulcke and co-authors (DOI to be added upon acceptance of publication).
Please cite our work when using these data.
Also acknowledge the following collections: The five beech discs used as in this paper were collected by the Institute for Nature and Forest Research (INBO: https://ror.org/00j54wy13) as part of the Intensive Monitoring Forest Ecosystems Measurement Network (International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests level II). These discs (Tw81397, Tw81403, Tw81405, Tw81408, Tw81410) are part of the Tervuren xylarium, the wood collection curated at the Royal Museum for Central Africa, Belgium (https://ror.org/001805t51).}},
author = {{Van den Bulcke, Jan and De Blaere, Ruben and Verschuren, Louis and Vansuyt, Simon}},
keywords = {{wood anatomy,gigapixel imaging,deep learning}},
publisher = {{BioImage Archive}},
title = {{Images accompanying the paper "Enabling high-throughput quantitative wood anatomy through a dedicated pipeline"}},
url = {{http://doi.org/10.6019/S-BIAD1574}},
year = {{2025}},
}
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