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001009063 0247_ $$2doi$$a10.34133/plantphenomics.0076
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001009063 1001_ $$0P:(DE-Juel1)179508$$aSelzner, Tobias$$b0$$eCorresponding author$$ufzj
001009063 245__ $$a3D U-Net Segmentation Improves Root System Reconstruction from 3D MRI Images in Automated and Manual Virtual Reality Work Flows
001009063 260__ $$aWashington, D.C.$$bAmerican Association for the Advancement of Science$$c2023
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001009063 520__ $$aMagnetic 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.
001009063 536__ $$0G:(DE-HGF)POF4-2173$$a2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217)$$cPOF4-217$$fPOF IV$$x0
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001009063 7001_ $$0P:(DE-HGF)0$$aHorn, Jannis$$b1
001009063 7001_ $$0P:(DE-Juel1)165987$$aLandl, Magdalena$$b2$$ufzj
001009063 7001_ $$0P:(DE-Juel1)129521$$aPohlmeier, Andreas$$b3$$ufzj
001009063 7001_ $$0P:(DE-Juel1)185995$$aHelmrich, Dirk$$b4$$ufzj
001009063 7001_ $$0P:(DE-Juel1)144686$$aHuber, Katrin$$b5
001009063 7001_ $$0P:(DE-Juel1)129548$$aVanderborght, Jan$$b6$$ufzj
001009063 7001_ $$0P:(DE-Juel1)129549$$aVereecken, Harry$$b7$$ufzj
001009063 7001_ $$0P:(DE-Juel1)186817$$aBehnke, Sven$$b8
001009063 7001_ $$0P:(DE-Juel1)157922$$aSchnepf, Andrea$$b9$$ufzj
001009063 773__ $$0PERI:(DE-600)2968615-5$$a10.34133/plantphenomics.0076$$gVol. 5, p. 0076$$p0076$$tPlant phenomics$$v5$$x2097-0374$$y2023
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