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OAH-Net:一个深度神经网络,用于高效和强大的全息重建,用于离轴数字全息显微镜

Wei Liu¹, Kerem Delikoyun^2,3, Qianyu Chen^2,3

¹Bioinformatics Institute, Agency for Science, Technology and Research, 30 Biopolis Street, 138671, Singapore.

Biomedical optics express

|March 20, 2025

概括

我们开发了一种深度学习方法,OAH-Net,从离轴数字全息显微镜数据快速重建3D图像. 这种人工智能方法显著加快了细胞成像分析的速度,超过了显微镜的获取率.

科学领域:

生物物理学的生物物理.
计算成像技术的成像
机器学习机器学习

背景情况:

离轴数字全息显微镜 (OAHM) 为细胞研究提供高通量,无标签的3D成像.
全息图重建是一个主要的瓶,限制了OAHM的及时数据分析.
现有的方法难以达到实时应用所需的速度.

研究的目的:

为OAHM开发一种新的,加速的全息图重建方法.
将全息的物理原理与深度学习相结合,以提高准确性和速度.
为了实时实现全息数据的端到端分析,用于生物和医学应用.

主要方法:

提出了一种新的深度学习方法,即离轴全息图网络 (OAH-Net).
使用离轴全息的物理原理初始化网络重量.
通过对全息数据进行大规模学习,微调网络.

主要成果:

在硬件诱导的测量误差范围内,OAH-Net可以重建相位和振幅图像.
重建速度明显超过了显微镜的获取率.
在未见的稀释全血样本上表现出强烈的概括性.

结论:

在OAHM中,OAH-Net为全息图重建提供了一个快速而准确的解决方案.
该方法实现了实时分析功能,克服了以前的限制.
能够与其他模型无集成,实现先进的端到端全息数据分析.

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