Han Jiashu, Liu Kunzan, Isaacson Keith B, Monakhova Kristina, Griffith Linda G, You Sixian
Research Laboratory of Electronics, MIT, Cambridge, MA, USA.
Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA.
Nat Commun. 2025 Jan 16;16(1):745. doi: 10.1038/s41467-025-56078-4.
Three-dimensional subcellular imaging is essential for biomedical research, but the diffraction limit of optical microscopy compromises axial resolution, hindering accurate three-dimensional structural analysis. This challenge is particularly pronounced in label-free imaging of thick, heterogeneous tissues, where assumptions about data distribution (e.g. sparsity, label-specific distribution, and lateral-axial similarity) and system priors (e.g. independent and identically distributed noise and linear shift-invariant point-spread functions are often invalid. Here, we introduce SSAI-3D, a weakly physics-informed, domain-shift-resistant framework for robust isotropic three-dimensional imaging. SSAI-3D enables robust axial deblurring by generating a diverse, noise-resilient, sample-informed training dataset and sparsely fine-tuning a large pre-trained blind deblurring network. SSAI-3D is applied to label-free nonlinear imaging of living organoids, freshly excised human endometrium tissue, and mouse whisker pads, and further validated in publicly available ground-truth-paired experimental datasets of three-dimensional heterogeneous biological tissues with unknown blurring and noise across different microscopy systems.
三维亚细胞成像对于生物医学研究至关重要，但光学显微镜的衍射极限会影响轴向分辨率，从而阻碍准确的三维结构分析。这一挑战在厚的、异质组织的无标记成像中尤为突出，在这种情况下，关于数据分布（例如稀疏性、标签特异性分布和横向-轴向相似性）和系统先验（例如独立同分布噪声和线性平移不变点扩散函数）的假设往往无效。在此，我们介绍了SSAI-3D，这是一种弱物理信息、抗域转移的框架，用于稳健的各向同性三维成像。SSAI-3D通过生成多样化、抗噪声、样本知情的训练数据集并对大型预训练盲去模糊网络进行稀疏微调，实现稳健的轴向去模糊。SSAI-3D应用于活类器官、新鲜切除的人类子宫内膜组织和小鼠触须垫的无标记非线性成像，并在公开可用的具有未知模糊和噪声的三维异质生物组织的真实配对实验数据集上进一步验证，这些数据集来自不同的显微镜系统。
