Analysis of Arabidopsis Root Images - Studies on CNNs and Skeleton-Based Root Topology

Roots and their temporal development play an important role in plant research. Over the decades image-based monitoring of root growth has become a key methodology in this research field. The growing amount of image data is often tackled with automatic image analysis approaches. In particular convolutional neural networks (CNNs) recently gained increasing interest for root segmentation. This segmentation of roots is usually only the first step of an analysis pipeline and needs to be supplemented by topological reconstruction of the complete root system architecture. In this paper we present a comprehensive study of different CNN architectures, loss functions and parameter settings for root image segmentation. In addition, we show how main and lateral roots can be identified based on the skeletons of segmented root components as a first step towards topological reconstruction of root system architecture. We present quantitative and qualitative results on data released in the course of the CVPPA Arabidopsis Root Segmentation Challenge 2021.

Automated summary

This paper presents a comprehensive study on different CNN architectures, loss functions, and parameter settings for root image segmentation, as well as how main and lateral roots can be identified based on the skeletons of segmented root components. Results are demonstrated on data from the CVPPA Arabidopsis Root Segmentation Challenge 2021.