PT  - JOURNAL ARTICLE
AU  - Clément Douarre
AU  - Richard Schielein
AU  - Carole Frindel
AU  - Stefan Gerth
AU  - David Rousseau
TI  - Deep learning based root-soil segmentation from X-ray tomography images
AID  - 10.1101/071662
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 071662
4099  - http://biorxiv.org/content/early/2016/08/25/071662.short
4100  - http://biorxiv.org/content/early/2016/08/25/071662.full
AB  - 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.