Extended Data Fig. 6: Each pre-processing step and the CNN contributed to the accuracy of SUNS at the cost of lower speed.

We evaluated the contribution of each pre-processing option (spatial filtering, temporal filtering, and SNR normalization) and the CNN option to SUNS. The reference algorithm (SUNS) used all options except spatial filtering. We compared the performance of this reference algorithm to the performance with additional spatial filtering (optional SF), without temporal filtering (no TF), without SNR normalization (no SNR), and without the CNN (no CNN) when analyzing the ABO 275 μm dataset through ten-fold leave-one-out cross-validation. a, The recall, precision, and F₁ score of these variants. The temporal filtering, SNR normalization, and CNN each significantly contributed to the overall accuracy, but the impact of spatial filtering was not significant (*P < 0.05, **P < 0.005, n.s. - not significant; two-sided Wilcoxon signed-rank test, n = 10 videos; error bars are s.d.). The gray dots represent the scores on the test data for each round of cross-validation. b, The speed and F₁ score of these variants. Eliminating temporal filtering or the CNN significantly increased the speed, while adding spatial filtering or eliminating SNR normalization significantly lowered the speed (**P < 0.005; two-sided Wilcoxon signed-rank test, n = 10 videos; error bars are s.d.). The light color dots represent F₁ scores and speeds for the test data for each round of cross-validation. The execution of SNR normalization was fast (~0.07 ms/frame). However, eliminating SNR normalization led to a much lower optimal th_prob, and thus increased the number of active pixels and decreased precision. In addition, ‘no SNR’ had lower speed than the complete SUNS algorithm due to the increased post-processing computation workload for managing the additional active pixels and regions.