CORDIS OpenAire bioRxiv Sygma Bielefeld Academic Search Engine (BASE) Theses@asb Crossref Microsoft Academic Graph Organic Eprints Europe PubMed Central UnpayWall PURE Aarhus University PubMed Central Copenhagen University Research Information System European Union Open Data Portal https://www.biorxiv.org/content/10.1101/2020.04.16.044461v1.full.pdf https://nph.onlinelibrary.wiley.com/doi/pdf/10.1111/nph.18387 https://doi.org/10.1111/nph.18387 New Phytologist Open Access 2020-04-18 2022-07-19 2020-04-18 <p>We present RootPainter, a GUI-based software tool for the rapid training of deep neural networks for use in biological image analysis. RootPainter facilitates both fully-automatic and semi-automatic image segmentation. We investigate the effectiveness of RootPainter using three plant image datasets, evaluating its potential for root length extraction from chicory roots in soil, biopore counting and root nodule counting from scanned roots. We also use RootPainter to compare dense annotations to corrective ones which are added during the training based on the weaknesses of the current model.</p> Buildings and machinery 0301 basic medicine phenotyping root nodule biopore interactive machine learning Research segmentation deep learning rhizotron Breeding and genetics Machine Learning Soil 03 medical and health sciences Deep Learning GUI Farm nutrient management Image Processing, Computer-Assisted Neural Networks, Computer Smith, Abraham George, Han, Eusun, Petersen, Jens, Olsen, Niels Alvin Faircloth, Giese, Christian, Athmann, Miriam, Dresbøll, Dorte Bodin, Thorup‐Kristensen, Kristian, 2020-04-18 journalpaper <p>We present RootPainter, a GUI-based software tool for the rapid training of deep neural networks for use in biological image analysis. RootPainter facilitates both fully-automatic and semi-automatic image segmentation. We investigate the effectiveness of RootPainter using three plant image datasets, evaluating its potential for root length extraction from chicory roots in soil, biopore counting and root nodule counting from scanned roots. We also use RootPainter to compare dense annotations to corrective ones which are added during the training based on the weaknesses of the current model.</p> RootPainter: deep learning segmentation of biological images with corrective annotation 10.1111/nph.18387