Multispectral imaging and computational fusion for virtual staining with extended depth-of-field

Although virtual staining has emerged as a promising alternative to chemical staining through strong feature extraction and color representation capabilities of artificial intelligence, most methods suffer from poor robustness and limited compatibility with the existing clinic workflow. In this Letter, we propose a deep learned label-free virtual staining method to realize accurate and plug-and-play pathological examination with extended depth-of-field by encoding inherent spectral priors into stained visual representations. A custom imaging system with highly flexible optical parameters is constructed for multidimensional pathological spectral information acquisition. An end-to-end supervised spectral stained network (SSNet) is established for efficient spectral cue extraction and accurate stained feature learning. Experimental results across various tissues indicate that the proposed approach achieves robust virtual staining with high morphological accuracy and color fidelity. The proposed method completely gets rid of the utilization of exogenous dyes before imaging, which provides a new panel for fast diagnosis and <i>in-vivo</i> examination.