FibroTrack:一个独立的深度学习平台,用于在肌肉和心脏组织学中自动化定量纤维化
Anas Odeh1, Rahaf Salem2, Maher Abu Saleh3
1Department of Genetics and Developmental Biology, The Rappaport Faculty of Medicine and Research Institute, Technion Israel Institute of Technology, Haifa, Israel. Inass.odeh@campus.technion.ac.il.
FibroTrack是一个新的深度学习工具,可以准确量化肌肉和心脏组织中的纤维化. 这种自动化平台提高了纤维化分析在研究和病理学中的速度和可重复性.
科学领域:
- 生物医学工程 生物医学工程
- 计算病理学计算病理学
- 历史学 历史学 历史学
背景情况:
- 精确的纤维化量化对于诊断和理解肌肉和心脏疾病至关重要.
- 目前用于纤维化分析的手动和半自动方法耗时,主观,缺乏可重复性.
研究的目的:
- 推出FibroTrack,这是一个深度学习平台,旨在简化和准确量化纤维化.
- 为了验证FibroTrack在各种组织学染色 (Sirius Red,Masson's Trichrome,IHC) 的性能.
主要方法:
- 开发FibroTrack,这是一个独立的深度学习平台,具有GUI.
- 整合了LAB颜色空间规范化和你只看一次版本11 (YOLOv11) 细分模型.
- 在2,034张组织图像上训练模型,以便进行强大的纤维化细分.
主要成果:
- 实现了99.5%的面具精度,用于肌肉细分.
- 与失明的病理学家有很高的一致性 (斯皮尔曼相关性:r = 0.87-0.96).
- 自动输出包括细分图像和结构化电子表格,确保可重复性.
结论:
- FibroTrack为高通量纤维化定量提供了一种新,准确和临床相关的解决方案.
- 该平台提高了纤维化分析的可复制性和可扩展性,用于临床前研究和病理学实践.
- 结合了先进的色彩分析与最先进的细分在一个可访问的工具.
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