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组织样本的极化辅助多域虚拟遗传病理染色

Mingzhou Jiang, Jiahao Fan, Nan Zeng

|October 1, 2025

概括

极化成像与生成对抗网络相结合,可以实现虚拟的病学染色. 这种新方法,Polar-starGAN,准确地将极化图像转化为数字病理学的各种染色.

科学领域:

生物医学光学生物医学光学
数字病理学数字病理学
医学中的人工智能

背景情况:

极化成像对组织结构具有很高的灵敏度,对生物医学研究至关重要.
穆勒矩阵显微镜和生成对抗网络 (GAN) 能够对组织样本进行虚拟染色.
现有的方法在域到域虚拟染色方面表现有前途.

研究的目的:

提出Polar-starGAN,一个新的两极化辅助多域虚拟遗传病理染色网络.
为了利用穆勒矩阵偏振图像进行精确的组织样本染色.
为了使基因病理图像的跨域转换.

主要方法:

开发极星GAN,一个集成极化信息的生成对抗网络.
使用穆勒矩阵偏振图像进行无监督的多域虚拟染色.
采用一个定制的无监督的生成对抗训练策略.

主要成果:

极星GAN成功地从H&E染色的偏振图像进行了跨域转换,并将其转换为其他三种染色方法.
生成的虚拟染色图像表现出高度的结构一致性.
生成的图像的色调与真实体病学幻灯片非常相匹配.
在人类胃肠组织切片上证明有效性.

结论:

极星GAN为数字病理学提供了有效的辅助工具.
该方法通过启用虚拟染色来增强临床病原体学诊断.
这种方法弥合了极化成像和虚拟组织病理学的桥梁,用于先进的诊断.

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