Related Experiment Video
Updated: Dec 12, 2025
Using High Resolution Computed Tomography to Visualize the Three Dimensional Structure and Function of Plant Vasculature
Published on: April 5, 2013
Digitally deconstructing leaves in 3D using X-ray microcomputed tomography and machine learning
Guillaume Théroux-Rancourt1, Matthew R Jenkins2, Craig R Brodersen3
1Institute of Botany University of Natural Resources and Life Sciences Vienna Austria.
Premise:
X-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 Results:
We 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.
Conclusions:
Overall, this 3D segmentation and quantification pipeline can reduce one of the major barriers to using microCT imaging in high-throughput plant phenotyping.
