单色数字H&E染色与进出网的染色.
Mengkun Chen1, Yen-Tung Liu1, Fadeel Sher Khan1
1University of Texas at Austin, Department of Biomedical Engineering, 107 W Dean Keeton St, Austin, 78712, TX, United States.
概括
本研究介绍了In-and-Out Net,这是一种用于数字染色的新型生成对抗网络 (GAN). 该模型有效地将反射同焦显微镜 (RCM) 图像转换为现实的血素和素 (H&E) 染色图像,帮助组织分析.
科学领域:
- 数字病理学数字病理学
- 组织学图像分析分析.
- 医学成像医学成像
背景情况:
- 传统的组织学染色是耗时的,需要广泛的基础设施.
- 数字染色为生成染色图像提供了一种高效,低基础设施的替代方案.
- 对临床医生来说,解释非传统的显微镜图像 (灰度,伪色) 是一个挑战.
研究的目的:
- 开发一种用于数字染色的新型网络,特别是将反射同焦显微镜 (RCM) 图像转换为血素和素 (H&E) 染色图像.
- 为病理学家和外科医生解决解释非传统显微镜图像的挑战.
主要方法:
- 开发了进出网,一种生成对抗网络 (GAN) 模型.
- 使用化预处理来增强皮肤组织的RCM图像中的核对比度.
- 用两个光通道的数字H&E标签训练模型,确保像素级的地面真实性而不需要图像注册.
主要成果:
- 进出网模型有效地将RCM图像转换为H&E染色图像.
- 在数字染色任务中实现了最先进的性能,通过比较分析和废除研究进行验证.
- 成功生成完美匹配的输入和地面真相图像,无需注册.
结论:
- 进出网为数字染色提供了有价值的工具,增强了组织学图像分析.
- 拟议的方法简化了组织分析,通过从RCM图像生成现实的H&E染色.
- 这一进步有助于在数字病理学中更快,更容易获得的组织解释.
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