Extended Data Fig. 4: The performance of SUNS was better than that of other methods in the presence of intensity noise or motion artifacts.
From: Segmentation of neurons from fluorescence calcium recordings beyond real time
The (a, d) recall, (b, e) precision, and (c, f) F1 score of all the (a-c) batch and (d-f) online segmentation algorithms in the presence of increasing intensity noise. The test dataset was the ABO 275 μm data with added random noise. The relative noise strength was represented by the ratio of the standard deviation of the random noise amplitude to the mean fluorescence intensity. As expected, the F1 scores of all methods decreased as the noise amplitude grew. The F1 of SUNS was greater than the F1’s of all other methods at all noise intensities. g-l, are in the same format of (a-f), but show the performance with the presence of increasing motion artifacts. The motion artifacts strength was represented by the standard deviation of the random movement amplitude (unit: pixels). As expected, the F1 scores of all methods decreased as the motion artifacts became stronger. The F1 of SUNS was greater than the F1’s of all other methods at all motion amplitudes. STNeuroNet and CaImAn batch were the most sensitive to strong motion artifacts, likely because they rely on accurate 3D spatiotemporal structures of the video. On the contrary, SUNS relied more on the 2D spatial structure, so it retained the accuracy better when spatial structures changed position over different frames.
