arXiv 2411.187952025-09-09cs.CV

GloFinder：AI赋能的QuPath插件，用于全切片级别的肾小球检测、可视化与整理

GloFinder: AI-empowered QuPath Plugin for WSI-level Glomerular Detection, Visualization, and Curation

Department of Electrical and Computer Engineering, Vanderbilt University Medical Center（电气与计算机工程系，范德比尔特大学医学中心）；Department of Computer Science, Vanderbilt University（计算机科学系，范德比尔特大学）；Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center（病理学、微生物学与免疫学系，范德比尔特大学医学中心）

Jialin Yue, Tianyuan Yao, Ruining Deng, Siqi Lu, Junlin Guo, Quan Liu, Mengmeng Yin, Juming Xiong, Haichun Yang, Yuankai Huo

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AI总结：

GloFinder是QuPath插件，利用CircleNet和加权圆形融合实现全切片肾小球一键检测、可视化与编辑，提升临床可用性。

摘要：

人工智能（AI）在肾脏病理学中已展现出从全切片图像（WSI）自动检测肾小球（肾脏的关键功能单位）方面的显著成功。然而，现有的开源工具通常以源代码或Docker容器的形式分发，需要高级编程技能，这阻碍了非程序员（如临床医生）的使用。此外，当前模型通常在单一数据集上训练，且缺乏调整预测置信度水平的灵活性。为克服这些挑战，我们引入了GloFinder，一个QuPath插件，专为通过图形用户界面（GUI）进行在线编辑、实现全切片肾小球一键自动检测而设计。GloFinder采用CircleNet，一种利用圆形表示进行精确目标定位的无锚点检测框架，其模型基于约160,000个手动标注的肾小球进行训练。为进一步提升准确性，该插件整合了加权圆形融合（WCF），一种集成方法，结合多个CircleNet模型的置信度分数以生成精炼预测，在肾小球检测中实现了优越性能。GloFinder支持在QuPath中直接可视化和编辑结果，促进临床医生的无缝交互，并为肾脏病理学研究和临床实践提供了强大工具。代码和QuPath插件可在https://github.com/hrlblab/GloFinder获取。

英文摘要：

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

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。