  Structural reconstruction of plant roots from MRI is challenging, because of
low resolution and low signal-to-noise ratio of the 3D measurements which may
lead to disconnectivities and wrongly connected roots. We propose a two-stage
approach for this task. The first stage is based on semantic root vs. soil
segmentation and finds lowest-cost paths from any root voxel to the shoot. The
second stage takes the largest fully connected component generated in the first
stage and uses 3D skeletonization to extract a graph structure. We evaluate our
method on 22 MRI scans and compare to human expert reconstructions.
