基于生成对抗网络的数字染色转换,用于从超光谱H&E染色图像生成RGB EVG染色图像
Tanwi Biswas1, Hiroyuki Suzuki2, Masahiro Ishikawa3
1Tokyo Institute of Technology, Department of Information and Communications Engineering, Tokyo, Japan.
Journal of biomedical optics
|June 2, 2023
概括
这项研究引入了一种深度学习方法,可以从血素和素 (H&E) 染色的高光谱图像中创建Verhoeff的van Gieson (EVG) 染色图像. 与用于弹性纤维量化的传统EVG染色相比,这种数字染色转换节省了时间和成本.
科学领域:
- 数字病理学数字病理学
- 计算机成像成像技术
- 生物医学图像分析
背景情况:
- 弹性纤维的量化对于疾病诊断至关重要.
- 血素和欧 (H&E) 染色是成本高效的,但不能区分弹性纤维.
- 传统的Verhoeff's van Gieson (EVG) 染色是昂贵且耗时的.
研究的目的:
- 开发基于深度学习的计算机化方法,从超光谱H&E染色图像生成RGB EVG染色图像.
- 为了减少与传统的EVG染色程序相关的时间和成本.
主要方法:
- 使用高光谱H&E染色图像和RGB EVG染色全片图像的人类胰腺组织.
- 采用基于CycleGAN的深度学习模型,用于不同模式 (超光谱和RGB) 和频道维度之间的数字染色转换.
- 引入了一组三个基本函数来计算损失组件,在H&E图像的减少通道维度内保留EVG图像特征.
主要成果:
- 一组三种基本功能,包括线性分辨器功能和欧和血氧的传导光谱,有效地保留了弹性纤维的特性,以便从原蛋白中进行区分.
- 拟议的训练方法需要较少的配对训练数据来生成具有精确弹性纤维识别的真实EVG染色图像.
- 该模型实现了同时将超光谱转换为RGB和H&E转换为EVG的图像,在生成现实的RGB EVG图像方面表现出有效性.
结论:
- 开发的深度学习方法成功地从超光谱H&E染色图像中生成现实的RGB EVG染色图像.
- 经过故意设计的三种基本功能的组合,在保留相关信息以准确识别弹性纤维方面被证明是有效的.
- 这种数字方法在具有成本效益和时间效率的组织分析中为病理诊断提供了显著的进步.
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