Iterative image segmentation of plant roots for high-throughput phenotyping

Abstract

Accurate segmentation of root system architecture (RSA) from 2D images is an important step in studying phenotypic traits of root systems. Various approaches to image segmentation exist but many of them are not well suited to the thin and reticulated structures characteristic of root systems. The findings presented here describe an approach to RSA segmentation that takes advantage of the inherent structural properties of the root system, a segmentation network architecture we call ITErRoot. We have also generated a novel 2D root image dataset which utilizes an annotation tool developed for producing high quality ground truth segmentation of root systems. Our approach makes use of an iterative neural network architecture to leverage the thin and highly branched properties of root systems for accurate segmentation. Rigorous analysis of model properties was carried out to obtain a high-quality model for 2D root segmentation. Results show a significant improvement over other recent approaches to root segmentation. Validation results show that the model generalizes to plant species with fine and highly branched RSA's, and performs particularly well in the presence of non-root objects.

Plant phenotyping relevance

植物根系画像からRSAを抽出するセグメンテーション手法を開発し、データセット作成と他手法との検証・比較を行っており、植物フェノタイピング手法が中心です。

Code and data availability

The paper's Data availability statement provides public GitHub repositories for the authors' ITErRoot training code and the Friendly Ground Truth annotation tool used to create the paper's root segmentation ground truth. Both are paper-specific, public, and actionable. No separate phenotype image dataset deposit URL is