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Analyzing plant roots is crucial to understand plant performance in differentsoil environments. While magnetic resonance imaging (MRI) can be used to obtain3D images of plant roots, extracting the root structural model is challengingdue to highly noisy soil environments and low-resolution of MRI images. Toimprove both contrast and resolution, we adapt the state-of-the-art methodRefineNet for 3D segmentation of the plant root MRI images in super-resolution.The networks are trained from few manual segmentations that are augmented bygeometric transformations, realistic noise, and other variabilities. Theresulting segmentations contain most root structures, including branches notextracted by the human annotator.

Learning Super-resolution 3D Segmentation of Plant Root MRI Images from Few Examples