TY  - JOUR
AU  - Selzner, Tobias
AU  - Horn, Jannis
AU  - Landl, Magdalena
AU  - Pohlmeier, Andreas
AU  - Helmrich, Dirk
AU  - Huber, Katrin
AU  - Vanderborght, Jan
AU  - Vereecken, Harry
AU  - Behnke, Sven
AU  - Schnepf, Andrea
TI  - 3D U-Net Segmentation Improves Root System Reconstruction from 3D MRI Images in Automated and Manual Virtual Reality Work Flows
JO  - Plant phenomics
VL  - 5
SN  - 2097-0374
CY  - Washington, D.C.
PB  - American Association for the Advancement of Science
M1  - FZJ-2023-02611
SP  - 0076
PY  - 2023
AB  - Magnetic resonance imaging (MRI) is used to image root systems grown in opaque soil. However, reconstruction of root system architecture (RSA) from 3-dimensional (3D) MRI images is challenging. Low resolution and poor contrast-to-noise ratios (CNRs) hinder automated reconstruction. Hence, manual reconstruction is still widely used. Here, we evaluate a novel 2-step work flow for automated RSA reconstruction. In the first step, a 3D U-Net segments MRI images into root and soil in super-resolution. In the second step, an automated tracing algorithm reconstructs the root systems from the segmented images. We evaluated the merits of both steps for an MRI dataset of 8 lupine root systems, by comparing the automated reconstructions to manual reconstructions of unaltered and segmented MRI images derived with a novel virtual reality system. We found that the U-Net segmentation offers profound benefits in manual reconstruction: reconstruction speed was doubled (+97%) for images with low CNR and increased by 27% for images with high CNR. Reconstructed root lengths were increased by 20% and 3%, respectively. Therefore, we propose to use U-Net segmentation as a principal image preprocessing step in manual work flows. The root length derived by the tracing algorithm was lower than in both manual reconstruction methods, but segmentation allowed automated processing of otherwise not readily usable MRI images. Nonetheless, model-based functional root traits revealed similar hydraulic behavior of automated and manual reconstructions. Future studies will aim to establish a hybrid work flow that utilizes automated reconstructions as scaffolds that can be manually corrected.
LB  - PUB:(DE-HGF)16
C6  - 37519934
UR  - <Go to ISI:>//WOS:001124487900003
DO  - DOI:10.34133/plantphenomics.0076
UR  - https://juser.fz-juelich.de/record/1009063
ER  -