Self-supervised denoising for enhanced volumetric reconstruction and signal interpretation in two-photon microscopy

Volumetric imaging is increasingly in demand for its precision in statistically visualizing and analyzing the intricacies of biological phenomena. To visualize the intricate details of these minute structures and facilitate the analysis in biomedical research, high-signal-to-noise ratio (SNR) images are indispensable. However, the inevitable noise presents a significant barrier to imaging qualities. Here, we propose SelfMirror, a self-supervised deep-learning denoising method for volumetric image reconstruction. SelfMirror is developed based on the insight that the variation of biological structure is continuous and smooth; when the sampling interval in volumetric imaging is sufficiently small, the similarity of neighboring slices in terms of the spatial structure becomes apparent. Such similarity can be used to train our proposed network to revive the signals and suppress the noise accurately. The denoising performance of SelfMirror exhibits remarkable robustness and fidelity even in extremely low-SNR conditions. We demonstrate the broad applicability of SelfMirror on multiple imaging modalities, including two-photon microscopy, confocal microscopy, expansion microscopy, computed tomography, and 3D electron microscopy. This versatility extends from single neuron cells to tissues and organs, highlighting SelfMirror’s potential for integration into diverse imaging and analysis pipelines.