(A) An example synthetic kymograph and its corresponding ground truth, manual control, the prediction by KymoButler, and the prediction by Fourier filtering. The top row depicts individual tracks in different colours and the bottom row shows the prediction overlay (magenta) with the ground truth (green) for all approaches. Discrepancies are thus highlighted in magenta (false positive) and green (false negative), while matching ground truth and prediction appears white. (B) Schematic explaining the concept of recall and precision. The top row depicts the possible deviations of the prediction from the ground truth. The middle and bottom rows show example overlays, again in green and magenta, from the synthetic data. In the left column, the prediction is larger than the ground truth (magenta is visible) leading to false positive pixels and low track precision, but a small number of false negatives and thus high track recall. An example prediction overlay of the Fourier filter approach is shown, which tends to elongate track ends. The right column shows a shorter prediction than the ground truth, leading to green segments in the overlay. While this prediction has high track precision (low number of false positive pixels), track recall is low due to the large number of false negatives. Again, a cut-out from the Fourier filter prediction is shown, where multiple gaps are introduced in tracks, thus severely diminishing track recall (see Material and methods for a detailed explanation of recall and precision). The middle column shows the same two cut outs analysed by KymoButler. No magenta or green segments are visible, thus leading to high recall and precision. (C) Synthetic kymograph region with four gaps highlighted (arrow heads): in one or more kymograph image rows the signal was artificially eliminated but kept in the ground truth to simulate real fluorescence data. While KymoButler efficiently connects tracks over gaps, the Fourier filter is unable to do so and breaks up those tracks into segments or incorrectly shortens these tracks (red arrow heads). Yellow arrow heads depict correct gap bridging events. (D) A synthetic kymograph with several line crossings. While KymoButler efficiently resolved all crossings, that is lines that cross other lines are not broken up into two segments, the Fourier filter correctly identifies the line crossing at the yellow arrow head but erroneously terminates the red and yellow tracks at the red arrow head. (E) The geometric means of recall and precision (‘track F1 score’) for KymoButler, the Fourier filter approach, and manual control. Each dot represents the average track F1 score of one synthetic kymograph (, Kruskal-Wallis Test, Tukey post-hoc: manual vs KymoButler , manual vs Fourier Filtering ). (F) Quantification of gap bridging performance for KymoButler (89%), manual control (88%), and Fourier filter (72%); lines: medians of all 10 synthetic kymographs, , Kruskal-Wallis Test, Tukey post-hoc: manual vs KymoButler , manual vs Fourier Filtering . (G) The fraction of correctly identified crossings for KymoButler, manual annotation, and the Fourier filter (88% KymoButler, 86% manual, 60% Fourier filter; lines: medians of all 10 synthetic kymographs, , Kruskal-Wallis Test, Tukey post-hoc: manual vs KymoButler , manual vs Fourier Filtering ). Tracks smaller than 3 pixels and shorter than 3 frames were discarded from the quantification.