  Analyzing plant roots is crucial to understand plant performance in different
soil environments. While magnetic resonance imaging (MRI) can be used to obtain
3D images of plant roots, extracting the root structural model is challenging
due to highly noisy soil environments and low-resolution of MRI images. To
improve both contrast and resolution, we adapt the state-of-the-art method
RefineNet for 3D segmentation of the plant root MRI images in super-resolution.
The networks are trained from few manual segmentations that are augmented by
geometric transformations, realistic noise, and other variabilities. The
resulting segmentations contain most root structures, including branches not
extracted by the human annotator.
