学习可迁移的自监督先验用于结构照明显微镜超分辨重建

中文摘要 AI 辅助

结构照明显微镜（SIM）扩展了光学通带，超出该通带的细节重建依赖于先验知识。手工设计的正则化器取决于其结构假设与样本的匹配程度；学习到的先验可能对成像条件和样本结构的变化敏感。我们引入了SIMAdapter，它通过自监督方式在来自BioSR、BioTISR以及跨越不同点扩散函数（PSF）和样本结构的模拟的23,237个原始SIM堆栈上预训练一个网络，该网络预测发射体和点扩散函数（PSF），然后将其适应到单个未标记的目标堆栈。适应过程根据可微的图像形成模型优化网络，其中光模式从该堆栈中校准。两个阶段都从原始测量中获取监督信号，无需配对的高分辨率参考。在两个保留的合成域上，SIMAdapter的平均发射体归一化均方根误差达到0.156，而Sparse-SIM为0.403。从仅基于BioSR预训练的网络开始的相同适应过程在两个域中均不太准确。在三个实验案例研究中，适应减少了侧翼伪影，并在丝状对、线粒体边界和校准线上产生了更清晰的轮廓。因此，单个预训练网络可以跨SIM测量重复使用，每次重建都根据其自身的原始数据进行优化。

英文摘要

Structured illumination microscopy (SIM) extends the optical passband, and reconstruction of detail beyond it depends on prior knowledge. Hand-designed regularizers depend on how well their structural assumptions match the specimen; learned priors can be sensitive to changes in imaging conditions and specimen structure. We introduce SIMAdapter, which pretrains a network that predicts the emitter and the point-spread function (PSF) by self-supervision on 23,237 raw SIM stacks from BioSR, BioTISR, and simulations spanning different PSFs and specimen structures, then adapts it to a single unlabeled target stack. Adaptation refines the network against a differentiable image-formation model, with the light pattern calibrated from that stack. Both stages take their supervision from the raw measurements and need no paired high-resolution reference. On two held-out synthetic domains, SIMAdapter reaches a mean emitter normalized root-mean-square error of 0.156, compared with 0.403 for Sparse-SIM. The same adaptation started from a network pretrained on BioSR alone is less accurate in both domains. In three experimental case studies, adaptation reduces flanking artifacts and yields more distinct profiles across filament pairs, mitochondrial boundaries, and calibration lines. A single pretrained network can thus be reused across SIM measurements, with each reconstruction refined against its own raw data.

发表机构

• University of Science and Technology of China（中国科学技术大学）
• CAS Center for Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China（中国科学院量子信息与量子物理前沿卓越中心）
• Anhui Province Key Laboratory of Quantum Network, University of Science and Technology of China（安徽省量子通信工程重点实验室）
• Hefei National Laboratory, University of Science and Technology of China（合肥国家实验室）

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