Extended Data Fig. 9: Changing the frequency of updating the neuron masks modulated trade-offs between SUNS online’s response time to new neurons and SUNS online’s performance metrics.
From: Segmentation of neurons from fluorescence calcium recordings beyond real time
The (a-c) F1 score and (d-f) speed of SUNS online increased as the number of frames per update (nmerge) increased for the (a, d) ABO 275 μm, (b, e) Neurofinder, and (c, f) CaImAn datasets. The solid line is the average, and the shading is one s.d. from the average (n = 10, 12, and 16 cross-validation iterations for the three datasets). In (a-c), the green lines show the F1 score (solid) ± one s.d. (dashed) of SUNS batch. The F1 score and speed generally increased as nmerge increased. For example, the F1 score and speed when using nmerge = 500 were respectively higher than the F1 score and speed when using nmerge = 20, and some of the differences were significant (*P < 0.05, **P < 0.005, ***P < 0.001, n.s. - not significant; two-sided Wilcoxon signed-rank test; n = 10, 12, and 16, respectively). We updated the baseline and noise regularly after initialization for the Neurofinder dataset, but did not do so for other datasets. The nmerge was inversely proportional to the update frequency or the responsiveness of SUNS online to the appearance of new neurons. A trade-off exists between this responsiveness and the accuracy and speed of SUNS online. At the cost of less responsiveness, a higher nmerge allowed the accumulation of temporal information and improved the accuracy of neuron segmentations. Likewise, a higher nmerge improved the speed because it reduced the occurrence of computations for aggregating neurons.
