TARSNet: Topology Aware Root Segmentation Network for plant phenotyping

Root morphological traits are key to monitoring plant growth and development. Traditionally, plant biologists relied on manual or semi-Automatic approaches to accurately estimate these traits. With high-Throughput acquisition of root image data, the computation of these root traits is currently achieved with automatic image analysis, and in this context, root segmentation is an important pre-processing step. However, this is a challenging task because of (1) diverse root characteristics i.e orientation, size and shape, (2) complex image background, (3) low contrast and (4) varying degrees of self-occlusion. Deep learning methods proposed for root segmentation have mainly focused on conventional pixel-wise losses. In addition, they neglected the relationship between deep features which is crucial for segmentation of thin root structures in the presence of complex backgrounds such as water droplets and leaves. In this paper, we propose a novel attention based framework that combines the strength of feature attention, topological loss and residual learning for root segmentation. The proposed framework has reached state-of-The-Art performance on Arabidopsis Root Segmentation Challenge 2021 dataset from Computer Vision in Plant Phenotyping and Agriculture (CVPPA). An ablation study has also been conducted to evaluate the contribution of each module to the proposed framework.