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

## TL;DR

This paper introduces a super-resolution 3D segmentation method for plant root MRI images that effectively utilizes few annotated examples and data augmentation to improve root structure extraction in noisy, low-resolution environments.

## Contribution

It adapts RefineNet for super-resolution 3D segmentation of plant roots using limited manual annotations and data augmentation techniques.

## Key findings

- Achieves accurate segmentation of root structures including branches.
- Effectively utilizes few annotated examples for training.
- Enhances contrast and resolution in noisy MRI images.

## Abstract

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.