% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Selzner:1009063,
      author       = {Selzner, Tobias and Horn, Jannis and Landl, Magdalena and
                      Pohlmeier, Andreas and Helmrich, Dirk and Huber, Katrin and
                      Vanderborght, Jan and Vereecken, Harry and Behnke, Sven and
                      Schnepf, Andrea},
      title        = {3{D} {U}-{N}et {S}egmentation {I}mproves {R}oot {S}ystem
                      {R}econstruction from 3{D} {MRI} {I}mages in {A}utomated and
                      {M}anual {V}irtual {R}eality {W}ork {F}lows},
      journal      = {Plant phenomics},
      volume       = {5},
      issn         = {2097-0374},
      address      = {Washington, D.C.},
      publisher    = {American Association for the Advancement of Science},
      reportid     = {FZJ-2023-02611},
      pages        = {0076},
      year         = {2023},
      abstract     = {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.},
      cin          = {IBG-3},
      ddc          = {580},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2173},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {37519934},
      UT           = {WOS:001124487900003},
      doi          = {10.34133/plantphenomics.0076},
      url          = {https://juser.fz-juelich.de/record/1009063},
}