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.

Keywords

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