ArticleApplications in plant sciences2020
Digitally deconstructing leaves in 3D using X-ray microcomputed tomography and machine learning.
Article in Applications in plant sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- High-resolution microCT reveals relationships between stomata and interior leaf anatomy in sorghum.Plant physiology · 2026Article
- Biochemical and anatomical leaf characteristics of oak trees contribute to differences in photosynthetic capacity between leaf habits.AoB PLANTS · 2025Article
- Rho GTPase overexpression impacts leaf internal architecture and mesophyll conductance in Arabidopsis.The New phytologist · 2025Article
- A vision of human-AI collaboration for enhanced biological collection curation and research.Bioscience · 2025Article
- Mind the leaf anatomy while taking ground truth with portable chlorophyll meters.Scientific reports · 2025Article
- Detection and characterization of spike architecture based on deep learning and X-ray computed tomography in barley.Plant methods · 2023Article
- How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques.Plant methods · 2023Review
- Creating a virtual leaf.AoB PLANTS · 2023Review
- Analyzing anatomy over three dimensions unpacks the differences in mesophyll diffusive area between sun and shadeAoB PLANTS · 2023Article
- Structural and functional leaf diversity lead to variability in photosynthetic capacity across a range of Juglans regia genotypes.Plant, cell & environment · 2022Article
- Desiccation of the leaf mesophyll and its implications for COPlant, cell & environment · 2022Article
- Desiccation tolerance in bryophytes relates to elasticity but is independent of cell wall thickness and photosynthesis.Physiologia plantarum · 2022Article
- X-ray microscopy enables multiscale high-resolution 3D imaging of plant cells, tissues, and organs.Plant physiology · 2022Article
- A workflow for segmenting soil and plant X-ray computed tomography images with deep learning in Google's Colaboratory.Frontiers in plant science · 2022Article
- Maximum COProceedings. Biological sciences · 2021Article
- Advances on the Visualization of the Internal Structures of the European Mistletoe: 3D Reconstruction Using Microtomography.Frontiers in plant science · 2021Article
- Digitally deconstructing leaves in 3D using X-ray microcomputed tomography and machine learning.Applications in plant sciences · 2020Article
- Machine learning: A powerful tool for gene function prediction in plants.Applications in plant sciences · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
premiseX-ray microcomputed tomography (microCT) can be used to measure 3D leaf internal anatomy, providing a holistic view of tissue organization. Previously, the substantial time needed for segmenting multiple tissues limited this technique to small data sets, restricting its utility for phenotyping experiments and limiting our confidence in the inferences of these studies due to low replication numbers. METHODS AND
resultsWe present a Python codebase for random forest machine learning segmentation and 3D leaf anatomical trait quantification that dramatically reduces the time required to process single-leaf microCT scans into detailed segmentations. By training the model on each scan using six hand-segmented image slices out of >1500 in the full leaf scan, it achieves >90% accuracy in background and tissue segmentation.
conclusionsOverall, this 3D segmentation and quantification pipeline can reduce one of the major barriers to using microCT imaging in high-throughput plant phenotyping.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.