Transfer learning from synthetic data applied to soil–root segmentation in X-ray tomography images

Clement Douarre, Richard Schielein, Carole Frindel, Stefan Gerth, David Rousseau

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

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.

Original languageEnglish
Article number65
JournalJournal of Imaging
Volume4
Issue number5
DOIs
Publication statusPublished - 2018
Externally publishedYes

Keywords

  • Root systems
  • Segmentation
  • Transfer learning
  • X-ray tomography

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering

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