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从使用卷积神经网络的全幻灯片多重化组织成像进行空间免疫类型鉴定.

Mohammad Yosofvand, Sharon N Edmiston, James W Smithy

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    深度学习管道CellGate自动化了多重复合免疫光 (mIF) 图像分析以发现生物标志物. 这种计算工具简化了瘤免疫微环境的分析,提高了临床研究的可扩展性.

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    科学领域:

    • 计算病理学计算病理学
    • 生物医学成像分析分析
    • 深度学习在瘤学中的应用.

    背景情况:

    • 多复合免疫光学 (mIF) 能够对瘤免疫微环境进行详细分析.
    • 目前的mIF图像分析是劳动密集型的,需要专门的病理学专业知识,限制了可扩展性.
    • 需要自动化计算管道来进行高效的mIF分析.

    研究的目的:

    • 开发和验证CellGate,这是一个深度学习 (DL) 计算管道,用于自动化,端到端的全幻灯片mIF图像分析.
    • 简化细胞核检测,细胞细分,细胞分类和免疫表型.
    • 提高mIF分析的可扩展性和临床应用.

    主要方法:

    • 开发了CellGate,一个DL管道用于mIF图像分析.
    • 在34名黑色素瘤患者的75万多张单细胞图像上训练了模型.
    • 在9种原发性黑色素瘤的独立队列中,通过全幻灯片mIF图像验证了管道.

    主要成果:

    • 在分析全新的mIF幻灯片时,CellGate展示了高精度回忆AUC.
    • 该管道准确地复制了独立黑色素瘤队伍的专家病理学分析.
    • 空间免疫表型揭示了对免疫细胞拓和T细胞状态的洞察力.

    结论:

    • CellGate提供了一个全自动化和可并行解决方案,用于整个幻灯片的mIF图像分析.
    • DL管道为细胞类型分类提供了更好的一致性和准确性.
    • CellGate有可能为大规模的临床和研究应用提供高通量mIF分析.