3D U-Net for Segmentation of Plant Root MRI Images in Super-Resolution

Yi Zhao; Nils Wandel; Magdalena Landl; Andrea Schnepf; Sven Behnke

3D U-Net for Segmentation of Plant Root MRI Images in Super-Resolution

Computer Vision and Pattern Recognition 2020-02-24 v1

Authors: Yi Zhao , Nils Wandel , Magdalena Landl , Andrea Schnepf , Sven Behnke

Abstract

Magnetic resonance imaging (MRI) enables plant scientists to non-invasively study root system development and root-soil interaction. Challenging recording conditions, such as low resolution and a high level of noise hamper the performance of traditional root extraction algorithms, though. We propose to increase signal-to-noise ratio and resolution by segmenting the scanned volumes into root and soil in super-resolution using a 3D U-Net. Tests on real data show that the trained network is capable to detect most roots successfully and even finds roots that were missed by human annotators. Our experiments show that the segmentation performance can be further improved with modifications of the loss function.

Keywords

magnetic resonance imaging plant root super-resolution 3d u-net image segmentation loss function

Cite

@article{arxiv.2002.09317,
  title  = {3D U-Net for Segmentation of Plant Root MRI Images in Super-Resolution},
  author = {Yi Zhao and Nils Wandel and Magdalena Landl and Andrea Schnepf and Sven Behnke},
  journal= {arXiv preprint arXiv:2002.09317},
  year   = {2020}
}

Comments

6 pages, 5 figures, in the 28th European Symposium on Artificial Neural Networks

3D U-Net for Segmentation of Plant Root MRI Images in Super-Resolution

Abstract

Keywords

Cite

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