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Using High Resolution Computed Tomography to Visualize the Three Dimensional Structure and Function of Plant Vasculature

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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.

Applications in Plant Sciences

|August 9, 2020

Summary

This summary is machine-generated.

This study introduces a Python tool for rapid 3D leaf anatomy analysis using X-ray microcomputed tomography (microCT). The machine learning approach significantly speeds up segmentation, enabling high-throughput plant phenotyping.

Keywords:

image segmentation microCT plant leaf internal anatomy plant phenotyping random forest classification

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Area of Science:

Plant biology
Imaging science
Computational biology

Background:

X-ray microcomputed tomography (microCT) enables 3D leaf internal anatomy measurement.
Previous segmentation methods were time-consuming, limiting microCT application in large-scale plant phenotyping.

Purpose of the Study:

To develop a rapid and accurate method for segmenting and quantifying 3D leaf anatomy from microCT data.
To overcome the limitations of manual segmentation for high-throughput plant phenotyping.

Main Methods:

Development of a Python codebase utilizing random forest machine learning for segmentation.
Training the machine learning model on a small subset of manually segmented image slices per scan.
Automated quantification of 3D leaf anatomical traits.

Main Results:

The developed codebase dramatically reduces processing time for single-leaf microCT scans.
Achieved >90% accuracy in background and tissue segmentation.
Enables detailed 3D segmentations from microCT data.

Conclusions:

The 3D segmentation and quantification pipeline significantly reduces a major barrier to microCT imaging in plant science.
Facilitates the use of microCT for high-throughput plant phenotyping experiments.
Increases confidence in phenotyping study inferences through higher replication numbers.