DeepD3, an open framework for automated quantification of dendritic spines
Fig 3
DeepD3 a versatile tool for quantification of dendritic spines in microscopy data.
A, Maximum intensity projection of the benchmark dataset, a 3D image stack of dendrites and dendritic spines of CA1 pyramidal neuron of an organotypic hippocampal slice culture (raw data, top). DeepD3-generated prediction maps of dendrite (magenta) and dendritic spines (green). Segmented 3D ROIs using the spine prediction map (bottom). Scale bar indicates 50 µm. B, Inter-rater reliability of N = 7 raters, who manually annotated the location of all dendritic spines in the benchmark dataset (see panel A). The matrix was generated by comparing rater pairs (y-axis = Rater 1, x-axis = Rater 2) using matched spine annotations (see Online Methods). The far right column indicates how many dendritic spines annotated by a given rater (y-axis, Rater 1) were identified by DeepD3 (x-axis, Rater 2). The bottom row indicates how many spines that were segmented by DeepD3 (here Rater 1) were also identified by a given human rater (here Rater 2). C, Linear correlation of the number of raters that identified a given spine and the average DeepD3 dendritic spine prediction probability at the center of the spine. Single points indicate the mean ± SEM, dashed line indicates the regression line. D, Frequency plot of the number of dendritic spines against the number of raters that identified a given spine (N = {1, …, 7}). Shown are the performances of DeepD3 (small green bars) and the raters (small gray bars). The bottom two bars plot the number of spines that were found by DeepD3 but none of the raters (single wide green bar) and those localized by a rater but not DeepD3 (single wide gray bar).