一键检测肾组织切片中所有肾小球，支持医生直接在界面编辑。
GloFinder: AI-empowered QuPath Plugin for WSI-level Glomerular Detection, Visualization, and Curation
- 基于圆表示的无锚框检测框架，精准定位肾小球。
- 融合多模型置信度得分，检测准确率显著提升。
- 无需编程，临床医生可直接在QuPath中可视化与修正结果。
人工智能在肾病理全切片图像（WSI）中自动检测肾小球方面已取得显著进展。然而，现有开源工具通常以源代码或Docker镜像形式发布，需高级编程能力，限制了非程序员（如临床医生）的使用。此外，现有模型多基于单一数据集训练，预测置信度难以调整。为此，我们提出GloFinder，一个专为QuPath设计的插件，实现单击操作即可完成整个WSI的肾小球自动检测，并支持通过图形界面在线编辑。GloFinder采用CircleNet——一种基于圆表示的无锚框检测框架，模型在约16万例人工标注肾小球上训练。为提升精度，引入加权圆融合（WCF）集成方法，融合多个CircleNet模型的置信度得分，生成更优预测结果。该插件可在QuPath中直接可视化并编辑检测结果，便于临床医生交互，为肾病病理研究与临床实践提供强大工具。代码与插件地址：https://github.com/hrlblab/GloFinder
原文摘要 · Abstract (English)
Artificial intelligence (AI) has demonstrated significant success in automating the detection of glomeruli, the key functional units of the kidney, from whole slide images (WSIs) in kidney pathology. However, existing open-source tools are often distributed as source code or Docker containers, requiring advanced programming skills that hinder accessibility for non-programmers, such as clinicians. Additionally, current models are typically trained on a single dataset and lack flexibility in adjusting confidence levels for predictions. To overcome these challenges, we introduce GloFinder, a QuPath plugin designed for single-click automated glomeruli detection across entire WSIs with online editing through the graphical user interface (GUI). GloFinder employs CircleNet, an anchor-free detection framework utilizing circle representations for precise object localization, with models trained on approximately 160,000 manually annotated glomeruli. To further enhance accuracy, the plugin incorporates Weighted Circle Fusion (WCF), an ensemble method that combines confidence scores from multiple CircleNet models to produce refined predictions, achieving superior performance in glomerular detection. GloFinder enables direct visualization and editing of results in QuPath, facilitating seamless interaction for clinicians and providing a powerful tool for nephropathology research and clinical practice. Code and the QuPath plugin are available at https://github.com/hrlblab/GloFinder
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