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J. Imaging 2018, 4(5), 65; https://doi.org/10.3390/jimaging4050065

Transfer Learning from Synthetic Data Applied to Soil–Root Segmentation in X-Ray Tomography Images

1
Laris, UMR INRA IRHS, Université d’Angers, 62 avenue Notre Dame du Lac, 49000 Angers, France
2
Development Center X-Ray Technology EZRT, Fraunhofer Institute for Integrated Systems IIS, Flugplatzstraße 75, 90768 Fürth, Germany
3
CREATIS, Université Lyon1, CNRS UMR5220, INSERM U1206, INSA-Lyon, 69621 Villeurbanne, France
*
Author to whom correspondence should be addressed.
Received: 24 March 2018 / Revised: 23 April 2018 / Accepted: 1 May 2018 / Published: 6 May 2018
(This article belongs to the Special Issue AI Approaches to Biological Image Analysis)
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Abstract

One of the most challenging computer vision problems in the plant sciences is the segmentation of roots and soil in X-ray tomography. So far, this has been addressed using classical image analysis methods. In this paper, we address this soil–root segmentation problem in X-ray tomography using a variant of supervised deep learning-based classification called transfer learning where the learning stage is based on simulated data. The robustness of this technique, tested for the first time with this plant science problem, is established using soil–roots with very low contrast in X-ray tomography. We also demonstrate the possibility of efficiently segmenting the root from the soil while learning using purely synthetic soil and roots. View Full-Text
Keywords: root systems; segmentation; X-ray tomography; transfer learning root systems; segmentation; X-ray tomography; transfer learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Douarre, C.; Schielein, R.; Frindel, C.; Gerth, S.; Rousseau, D. Transfer Learning from Synthetic Data Applied to Soil–Root Segmentation in X-Ray Tomography Images. J. Imaging 2018, 4, 65.

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