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Deep learning based root-soil segmentation from X-ray tomography images

Clément Douarre, Richard Schielein, Carole Frindel, Stefan Gerth, David Rousseau
doi: https://doi.org/10.1101/071662
Clément Douarre
aCREATIS, Université Lyon1, CNRS UMR5220, INSERM U1206, INSA-Lyon, 69621 Villeurbanne, France
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Richard Schielein
bDevelopment Center X-Ray Technology EZRT, Fraunhofer Institute for Integrated Systems IIS, Flugplatzstraße 75, 90768 Fürth, Germany
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Carole Frindel
aCREATIS, Université Lyon1, CNRS UMR5220, INSERM U1206, INSA-Lyon, 69621 Villeurbanne, France
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Stefan Gerth
bDevelopment Center X-Ray Technology EZRT, Fraunhofer Institute for Integrated Systems IIS, Flugplatzstraße 75, 90768 Fürth, Germany
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David Rousseau
aCREATIS, Université Lyon1, CNRS UMR5220, INSERM U1206, INSA-Lyon, 69621 Villeurbanne, France
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Abstract

One of the most challenging computer vision problem in plant sciences is the segmentation of root and soil from X-ray tomography. So far, this has been addressed from classical image analysis methods. In this paper, we address this root/soil segmentation problem from X-ray tomography using a new deep learning classification technique. The robustness of this technique, tested for the first time on this plant science problem, is established with root/soil presenting a very low contrast in X-ray tomography. We also demonstrate the possibility to segment efficiently root from soil while learning on purely synthetic soil and root.

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Posted August 25, 2016.
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Deep learning based root-soil segmentation from X-ray tomography images
Clément Douarre, Richard Schielein, Carole Frindel, Stefan Gerth, David Rousseau
bioRxiv 071662; doi: https://doi.org/10.1101/071662
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Deep learning based root-soil segmentation from X-ray tomography images
Clément Douarre, Richard Schielein, Carole Frindel, Stefan Gerth, David Rousseau
bioRxiv 071662; doi: https://doi.org/10.1101/071662

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