Learning Super-resolution 3D Segmentation of Plant Root MRI Images from Few Examples
This addresses the problem of analyzing plant roots in soil environments for agricultural or biological research, but it is incremental as it adapts an existing method.
The paper tackled the challenge of extracting 3D root structures from noisy, low-resolution MRI images by adapting RefineNet for super-resolution segmentation, achieving segmentations that include branches missed by human annotators.
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.