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超级CCM:一个开源的Python工具包用于对焦显微镜图像中角膜神经纤维的自动定量

Qincheng Qiao^1,2,3, Tongxin Ren^1,2, Li Chen¹

¹Department of Endocrinology and Metabolism, Qilu Hospital of Shandong University, Jinan, China.

Translational vision science & technology

|November 18, 2025

概括

超CCM是一个新的开源Python包用于自动化角膜共聚焦显微镜 (CCM) 图像分析. 它提供了对角膜神经形态的先进量化,改进了神经退行性疾病研究的现有工具.

科学领域:

眼科医生眼科眼科
神经科学是一个神经科学.
医疗成像医学成像

背景情况:

角膜共聚焦显微镜 (CCM) 对于通过角膜神经分析检测神经退行性疾病至关重要.
当前的自动化工具 (例如,ACCMetrics) 已经过时,缺乏可扩展性.
现有的深度学习模型通常是单一任务的,而不是开源的.

研究的目的:

介绍SuperCCM,这是一个完全自动化的,模块化的,开源的Python包,用于全面的CCM图像分析.
解决当前自动化CCM分析工具的局限性.
促进使用CCM检测神经退行性疾病的先进研究.

主要方法:

超级CCM集成了细分,骨架,拓建模和参数提取.
使用精细注释的数据集 (SuperCCM-FineSet) 进行培训细分模型.
采用多阶段培训和绩效评估,使用cDice和手册注释.

主要成果:

实现了高的细分精度 (clDice 0.879 与VGG-11 + U-Net).
证明与手册注释对形态定量化的强烈一致性.
在关键参数的类内相关性和错误率上表现优于ACCMetrics.

结论:

超级CCM为临床CCM图像分析提供了一个可扩展的开源框架.
能够准确,自动量化角膜神经参数.
支持新型深度学习模型的整合,并促进可重现的研究.

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