RootPainter: deep learning segmentation of biological images with corrective annotation.

Abstract

Convolutional neural networks (CNNs) are a powerful tool for plant image analysis, but challenges remain in making them more accessible to researchers without a machine-learning background. We present RootPainter, an open-source graphical user interface based software tool for the rapid training of deep neural networks for use in biological image analysis. We evaluate RootPainter by training models for root length extraction from chicory (Cichorium intybus L.) roots in soil, biopore counting, and root nodule counting. We also compare dense annotations with corrective ones that are added during the training process based on the weaknesses of the current model. Five out of six times the models trained using RootPainter with corrective annotations created within 2 h produced measurements strongly correlating with manual measurements. Model accuracy had a significant correlation with annotation duration, indicating further improvements could be obtained with extended annotation. Our results show that a deep-learning model can be trained to a high accuracy for the three respective datasets of varying target objects, background, and image quality with < 2 h of annotation time. They indicate that, when using RootPainter, for many datasets it is possible to annotate, train, and complete data processing within 1 d.

Plant phenotyping relevance

RootPainterは植物画像から根長・バイオポア・根粒を抽出する深層学習ソフトウェアであり、補正アノテーション、精度、手動測定との相関を評価しているため、植物フェノタイピング手法が中心です。

Code and data availability

The paper's Data availability statement explicitly deposits the authors' analysis software (client/server source code and installers on GitHub) and a Colab notebook, all with public URLs. The paper-specific phenotype/training datasets (nodules, biopores, roots) and trained models are on Zenodo (DOIs 10.5281/zenodo.3755