Digitally deconstructing leaves in 3D using X-ray microcomputed tomography and machine learning.

Abstract

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.

Plant phenotyping relevance

3DマイクロCT画像から葉の内部解剖形質を抽出する機械学習セグメンテーションと定量化パイプラインの開発が中心であり、植物フェノタイピングへの適用性も明示されている。

Code and data availability

The paper's authors publicly released their random forest segmentation/leaf-traits analysis code on GitHub and the microCT image dataset, hand-labeled training slices, and segmentation outputs on Zenodo.