用于荧光显微镜跨模态超分辨率的物理信息深度学习模型
Physics-Informed Deep Learning Model for Cross-Modality Super-Resolution in Fluorescence Microscopy
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中文总结 AI 辅助
研究针对荧光显微镜跨模态超分辨率问题，提出将显微镜特定点扩散函数信息纳入训练目标的物理信息生成对抗网络，经实验验证该方法能提高模型结构保真度和物理合理性。
中文摘要 AI 辅助
跨模态图像翻译为从低分辨率图像进行超分辨率荧光显微镜检查提供了一条途径，同时降低了光毒性和仪器要求。然而，纯数据驱动模型可能会产生视觉上合理但与光学图像形成不一致的输出。在此，我们提出了一种用于共聚焦到受激发射损耗（STED）图像翻译的物理信息生成对抗网络，该网络将显微镜特定的点扩散函数信息纳入训练目标。使用在不同实验日获取的人原代M2巨噬细胞中TOM20标记线粒体的有限配对共聚焦 - STED数据集评估了模拟和实验测量的点扩散函数（PSF）。使用基于参考和非基于参考的图像质量指标以及互补的频率和分布敏感分析来评估性能。非参考指标探测了与物理相关的图像属性，包括空间频率内容、对比度和信噪比行为。与非PSF基线相比，PSF引导的模型提高了结构保真度，减少了局部偏差，并与STED参考更接近，特别是在频域分析中。这些结果表明，光学先验可以提高用于跨模态超分辨率成像的生成显微镜模型的结构保真度和物理合理性。
英文摘要
Cross-modality image translation offers a route to super-resolution fluorescence microscopy from low-resolution images while reducing phototoxicity and instrumentation demands. However, purely data-driven models can produce visually plausible outputs that are inconsistent with optical image formation. Here, we propose a physics-informed generative adversarial network for confocal-to-STED image translation that incorporates microscope-specific point spread function information into the training objective. Simulated and experimentally measured PSFs were evaluated using a limited paired confocal-STED dataset of TOM20-labeled mitochondria in human primary M2 macrophages acquired across different experimental days. Performance was assessed using reference-based and non-reference-based image-quality metrics, together with complementary frequency- and distribution-sensitive analyses. The no-reference metrics probed physics-relevant image properties, including spatial-frequency content, contrast, and signal-to-noise behavior. PSF-guided models improved structural fidelity, reduced local deviations, and achieved closer agreement with STED references than non-PSF baselines, particularly in frequency-domain analyses. These results demonstrate that optical priors can improve the structural fidelity and physical plausibility of generative microscopy models for cross-modality super-resolution imaging.
