RootPainter: deep learning segmentation of biological images with corrective annotation

Research output: Contribution to journalJournal articlepeer-review

Documents

  • Fulltext

    Final published version, 4.28 MB, PDF document

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.

Original languageEnglish
JournalNew Phytologist
Volume236
Pages (from-to)774-791
Number of pages18
ISSN0028-646X
DOIs
Publication statusPublished - 2022

Bibliographical note

Publisher Copyright:
© 2022 The Authors. New Phytologist © 2022 New Phytologist Foundation.

    Research areas

  • biopore, deep learning, GUI, interactive machine learning, phenotyping, rhizotron, root nodule, segmentation

Number of downloads are based on statistics from Google Scholar and www.ku.dk


No data available

ID: 318438881