RT Journal Article
SR Electronic
T1 RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.04.16.044461
DO 10.1101/2020.04.16.044461
A1 Abraham George Smith
A1 Eusun Han
A1 Jens Petersen
A1 Niels Alvin Faircloth Olsen
A1 Christian Giese
A1 Miriam Athmann
A1 Dorte Bodin Dresbøll
A1 Kristian Thorup-Kristensen
YR 2020
UL http://biorxiv.org/content/early/2020/04/18/2020.04.16.044461.abstract
AB 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.Competing Interest StatementThe authors have declared no competing interest.