ArticleFrontiers in plant science2021
Mask, Train, Repeat! Artificial Intelligence for Quantitative Wood Anatomy.
Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Accelerated growth increases the somatic epimutation rate in trees.Nature communications · 2025Article
- Enabling high-throughput quantitative wood anatomy through a dedicated pipeline.Plant methods · 2025Article
- A protocol for high-quality sectioning for tree-ring anatomy.Frontiers in plant science · 2025Article
- Towards ROXAS AI: automatic multi-species ring boundaries segmentation as regression in anatomical images.Frontiers in plant science · 2025Article
- Tree ring segmentation using UNEt TRansformer neural network on stained microsections for quantitative wood anatomy.Frontiers in plant science · 2023Article
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8 authors.
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Abstract
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 (
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