RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation.

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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 (GUI) 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 to corrective ones which 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 two hours 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 less than two hours of annotation time. They indicate that when using RootPainter, for many datasets it is possible to annotate, train and complete data processing within one day.This article is protected by copyright. All rights reserved.