Accelerated Haustoria Segmentation Enables Rapid Gene Function Analysis in Cereal-Powdery Mildew Pathosystems.

Abstract

Reliable, high-throughput quantification of early fungal infection events is crucial for gene function studies, but it remains labor-intensive. We report an openly available pipeline that automates the detection of β-glucuronidase (GUS)-stained epidermal cells and the intracellular haustoria formed by powdery mildew on barley and wheat leaves. Whole-slide images are captured with a commercial scanner, focus-projected, tiled, and analyzed by deep-learning models trained on expertly annotated datasets. A You Only Look Once ( YOLO ) network identifies GUS-positive cells, and a companion segmentation model pinpoints haustoria within each cell; automatic focus-layer selection preserves fine structural detail. The workflow runs in minutes per slide on a single workstation and maintains near-perfect agreement with manual counts in both barley and wheat, demonstrating robust cross-species transferability. By delivering single-cell readouts with minimal user input, the pipeline enables rapid functional validation screens and supports large-scale phenotyping of cereal-powdery mildew interactions. [Formula: see text] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Plant phenotyping relevance

深層学習と画像処理により、感染植物細胞内のハウストリアを自動検出・セグメンテーションする公開パイプラインを開発し、手動計数および種間移植性を検証しているため、植物病害表現型の取得手法が中心である。

Code and data availability

公開論文であることは確認できましたが、現在の公式API・許可済み取得経路では本文を自動取得できませんでした。

No evidence-backed public reproduction asset is currently recorded.