  Magnetic resonance imaging (MRI) enables plant scientists to non-invasively
study root system development and root-soil interaction. Challenging recording
conditions, such as low resolution and a high level of noise hamper the
performance of traditional root extraction algorithms, though. We propose to
increase signal-to-noise ratio and resolution by segmenting the scanned volumes
into root and soil in super-resolution using a 3D U-Net. Tests on real data
show that the trained network is capable to detect most roots successfully and
even finds roots that were missed by human annotators. Our experiments show
that the segmentation performance can be further improved with modifications of
the loss function.
