Accurate and versatile 3D segmentation of plant tissues at cellular resolution

Abstract

Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge. Here, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells. PlantSeg employs a convolutional neural network to predict cell boundaries and graph partitioning to segment cells based on the neural network predictions. PlantSeg was trained on fixed and live plant organs imaged with confocal and light sheet microscopes. PlantSeg delivers accurate results and generalizes well across different tissues, scales, acquisition settings even on non plant samples. We present results of PlantSeg applications in diverse developmental contexts. PlantSeg is free and open-source, with both a command line and a user-friendly graphical interface.

Code and data availability

The paper publicly deposits all plant phenotyping image/ground-truth datasets on OSF (https://osf.io/uzq3w), including ovule, lateral root, meristem, and leaf confocal/light-sheet volumes with hand-curated segmentations, and releases the PlantSeg analysis code and pre-trained 3D U-Net models on GitHub.