Robust high-throughput phenotyping with deep segmentation enabled by a web-based annotator

Abstract

Abstract The abilities of plant biologists and breeders to characterize the genetic basis of physio-logical traits is limited by their abilities to obtain quantitative data representing precise details of trait variation, and particularly to collect this data at a high-throughput scale at low cost. Although deep learning methods have demonstrated unprecedented potential to automate plant phenotyping, these methods commonly rely on large training sets that can be time-consuming to generate. Intelligent algorithms have therefore been proposed to enhance the productivity of these annotations and reduce human efforts. We propose a high-throughput phenotyping system which features a Graphical User Interface (GUI) and a novel interactive segmentation algorithm: Semantic-Guided Interactive Object Segmentation (SGIOS). By providing a user-friendly interface and intelligent assistance with annotation, this system offers potential to streamline and accelerate the generation of training sets, reducing the effort required by the user. Our evaluation shows that our proposed SGIOS model requires fewer user inputs compared to the state-of-art models for interactive segmentation. As a case study in the use of the GUI applied for genetic discovery in plants, we present an example of results from a preliminary genome-wide association study (GWAS) of in planta regeneration in Populus trichocarpa (poplar). We further demonstrate that the inclusion of semantic prior map with SGIOS can accelerate the training process for future GWAS, using a sample of a dataset extracted from a poplar GWAS of in vitro regeneration. The capabilities of our phenotyping system surpass those of humans unassisted to rapidly and precisely phenotype our traits of interest. The scalability of this system enables large-scale phenomic screens that would otherwise be time-prohibitive, thereby providing increased power for GWAS, mutant screens, and other studies relying on large sample sizes to characterize the genetic basis of trait variation. Our user-friendly system can be used by researchers lacking a computational background, thus helping to democratize the use of deep segmentation as a tool for plant phenotyping.

Plant phenotyping relevance

植物フェノタイピング用のGUIと対話型画像セグメンテーション手法を開発・評価しており、表現型取得ワークフロー自体が中心である。

Code and data availability

The supplied blocks describe the IDEAS/SGIOS phenotyping system, LSC benchmarking, and poplar GWAS, but contain no authors' public deposit of the paper's images, annotations, trained models, or analysis code. The only public URLs are third-party tools (COCO Annotator, DEXTR AnnoTool, VGG VIA), cited prior datasets (LSC

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