Fig. 1

Principle and performance validation of DeepBID. a, Diagram of the data construction. The raw stack from in vivo brain imaging, comprising forward and backward scan lines, is segmented into input and target sub-stacks for 3D network training, a process seamlessly integrated into the model. Post-training, the pretrained model enables direct testing of unidirectional or bidirectional scan images without division. b, Example test outcomes showcasing astrocyte images with vibrations. The noise in the volume was suppressed, rendering deeper structures more distinct. c, Left, a synthetic distribution of neurons (blue) and vessels (red). Right, convolution with the point spread function of the two-photon system. d, Generated reference image using bidirectional scanning for evaluating image quality metrics. Forward (blue dashed line) and backward (red dashed line) scanning paths are collinear, ensuring high semantic relevance for self-supervised learning. Scanning lines in the same direction remain parallel. These noise-free images serve as benchmarks for network performance assessment. e, Raw data constructed by introducing mixed Poisson-Gaussian noise and motion drifts (indicated by yellow arrows). f, Long-timescale calcium fluctuations evoked by 70 isolated neurons. All traces were normalized, with prominent firings delineated between red dashed lines. Zoomed-in traces are featured in the right panel. g, Top, Tukey box-and-whisker plot illustrating Pearson correlations of calcium traces extracted from enhanced data versus raw noisy data with a two-tailed Wilcoxon matched-pairs signed rank test (n = 70). Bottom, Correlation augmentation post-denoising. Each line corresponds to a distinct recording. Scale bars, 50 μm in b, d, and e