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RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation

Smith, Abraham George; Han, Eusun; Petersen, Jens; Olsen, Niels Alvin Faircloth; Giese, Christian; Athmann, Miriam; Dresbøll, Dorte Bodin and Thorup-Kristensen, Kristian (2020) RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation. Bioarxiv, NA, NA-NA. [Completed]

[thumbnail of 2020.04.16.044461v2.full.pdf] PDF - Published Version - English
Available under License Creative Commons Attribution.



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 semiautomatic 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.

EPrint Type:Journal paper
Subjects: Animal husbandry > Breeding and genetics
Farming Systems > Buildings and machinery
Farming Systems > Farm nutrient management
Research affiliation: Germany > University of Bonn
Denmark > KU - University of Copenhagen
Deposited By: Smith, Mr Abraham George
ID Code:38500
Deposited On:21 Oct 2020 09:48
Last Modified:21 Oct 2020 09:48
Document Language:English
Refereed:Not peer-reviewed

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