富里埃图形显微镜图像增强与双模态深度学习
Lyes Bouchama1,2, Bernadette Dorizzi1, Marc Thellier3
1Samovar, Télécom SudParis, Institut Polytechnique de Paris, 91120 Palaiseau, France.
Biomedical optics express
|July 27, 2023
概括
一个新的双模U-Net增强了福里埃图形显微镜 (FPM) 图像中的对比度,改善了自动疾病检测. 这种方法可以弥补焦点变化,提高亚细胞区的可见性,以便进行准确的诊断.
科学领域:
- 数字病理学数字病理学
- 显微镜的使用方法
- 人工智能在诊断中的应用
背景情况:
- 整体幻灯片成像和富里埃光谱显微镜 (FPM) 提供了自动疾病检测的先进功能.
- 厚样本面临挑战,因为场深问题会导致相位图像的对比度变化,从而限制诊断准确度.
- 现有的方法在高分辨率显微镜中难以保持亚细胞对比度和焦点精度.
研究的目的:
- 开发一种强大的方法来增强FPM图像中的亚细胞区对比度,特别是对于厚厚的生物样本.
- 通过补偿焦点变化和对比度变化来提高FPM相位图像的可用性.
- 证明开发的方法在改善疾病检测灵敏度而不影响特异性的有效性.
主要方法:
- 通过使用FPM的强度和相位图像来训练双模U-Net人工神经网络.
- 使用FPM重建算法和虚拟Z堆叠来选择最佳焦平面,构建了一个参考数据库.
- 训练了U-Net以同时增强目标亚细胞区的对比度,并纠正焦点不精确.
主要成果:
- 训练有素的U-Net在强度和相位图像中有效地增强了亚细胞区的可见性.
- 该方法在高分辨率的大视野中表现出稳健性.
- 在*Plasmodium falciparum*检测的用例中,在没有损失特异性的情况下获得了更好的检测灵敏度.
结论:
- 双模U-Net方法为FPM的后重建图像处理提供了显著的进步.
- 这种方法是通用的,适用于各种苛刻的生物查应用,需要高分辨率成像.
- 该技术通过提高图像质量和弥补显微镜中的光学限制来增强自动诊断.
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