Fig. 4

Model quality and segmentation precision: a The masks from SyMBac-trained models are truer to the geometry of the cells, displaying no aberration when compared to model outputs trained on human-annotated data. b The masks also maintain a narrow distribution of widths, while the masks from DeLTA trained with human-annotated data display a wide variation with the peak shifted to lower values and show 2.5× higher variation in cell width. c Examples of the type of data which can be segmented using a single SyMBac-trained model. In this case, we show the performance of a single DeLTA model trained on combination data across 3 different cell sizes. Scale bar = 2 μm d The SyMBac-trained model produces masks with precisions of 40 nm for length and 19 nm for width. This is calculated by fitting a line to the length and width trace of cells in the stationary phase