RT Journal Article
SR Electronic
T1 Deep learning based root-soil segmentation from X-ray tomography images
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 071662
DO 10.1101/071662
A1 Clément Douarre
A1 Richard Schielein
A1 Carole Frindel
A1 Stefan Gerth
A1 David Rousseau
YR 2016
UL http://biorxiv.org/content/early/2016/08/25/071662.abstract
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.