空气LEISH:一个数据集的Giemsa染色显微镜图像基于AI的Leishmania amastigotes检测检测
Rafeh Oualha1, Nesrine Fekih-Romdhane1, Donia Driss1,2
1Laboratory of Molecular Epidemiology and Experimental Pathology - LR16IPT04, Institut Pasteur de Tunis, Université de Tunis El Manar, Tunis, Tunisia.
Scientific data
|February 2, 2026
概括
一个名为AIR-LEISH的新数据集,可以帮助人工智能 (AI) 分析莱什曼病的微观图像. 该资源加速了用于检测寄生虫的AI开发,改善了药物发现和公共卫生工作.
科学领域:
- 寄生虫学的寄生虫学
- 医学昆虫学 医学昆虫学
- 计算生物学 计算生物学
背景情况:
- 莱什曼病是一种严重的寄生虫疾病,影响全球数以百万计的人,由沙传播.
- 显微镜是量化Leishmania寄生虫负担的标准,但是劳动密集型的,需要专业知识.
- 目前用于自动化寄生虫检测的AI应用程序受到注释数据集缺乏的阻碍.
研究的目的:
- 介绍AIR-LEISH,这是一个新的,专业注释的数据集,用于检测Leishmania巨虫.
- 促进人工智能驱动工具的开发,用于利什曼病的研究和药物发现.
- 为了使微观图像中的Leishmania amastigotes的自动分类,检测和计数.
主要方法:
- 创建AIR-LEISH:180张Giemsa染色的显微镜图像,注释了来自两个感染模型的8,140个Leishmania巨和1,511个巨.
- 注释专注于启用AI对象检测和图像分割任务.
- 使用AIR-LEISH数据集对YOLOv8和U-Net架构进行培训和测试.
主要成果:
- AIR-LEISH数据集包括180张注释图像,详细说明了寄生虫和宿主细胞的数量.
- YOLOv8和U-Net模型在分类,检测和计数Leishmania amastigotes方面显示出有希望的性能.
- 该数据集支持基于人工智能的分析,用于利什曼病研究.
结论:
- AIR-LEISH解决了在人工智能驱动的莱什曼病研究中对注释数据的关键需求.
- 数据集和展示的AI模型性能将加速自动化诊断和研究工具的开发.
- 在Zenodo上提供AIR-LEISH促进了合作,以促进公共卫生在打击莱什曼病方面的进展.
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