摘要
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.
基金
supported by the German Research Foundation(DFG)(grants BE 2556/15-1 and SCHN 1361/3-1)
partially funded by the DFG under Germany’s Excellence Strategy,EXC-2070-390732324-PhenoRob
the German Federal Ministry of Education and Research(BMBF)in the framework of the funding initiative Soil as a Sustainable Resource for the Bioeconomy BonaRes,the project BonaRes(Module A):Sustainable Subsoil Management-Soil3
subproject 3(grant 031B1066C).
作者简介
Address correspondence to:Tobias Selzner,t.selzner@fz-juelich.de
