Training datasets and final models from paper ''RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation'

Authors: Smith, Abraham Goerge; Han, Eusun; Petersen, Jens; Olsen, Niels Alvin Faircloth; Giese, Christian; Athmann, Miriam; Dresbøll, Dorte Bodin; +1 Authors

doi: 10.5281/zenodo.3754046 , 10.5281/zenodo.3754045

Training datasets and final models from paper ''RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation'

- Summary
- Subjects
- Metrics

Abstract

See paper ''RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation' for an explanation of how the models and datasets were created. The images are extracted from the following larger datasets using the RootPainter software: Nodules: http://doi.org/10.5281/zenodo.3753603 Biopores: http://doi.org/10.5281/zenodo.3753969 Roots: http://doi.org/10.5281/zenodo.3527713

Related Organizations

University of Copenhagen
Denmark
University of Bonn
Germany

Keywords

roots, root nodules, segmentation, deep learning, biopores

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	1
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average