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All images were filtered by the use of a median filter (kernel size = 3 × 3 pixels) to remove single pixel noise.
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Where M is a median filtering kernel with 3 × 3 pixels.
In addition, different filters such as weighted median filter, center weighted median filter, Gaussian filter, and others may be used instead of the median filter, but all of them provide similarly good results.
To correct for the uneven cytoplasmic background signal at the edge of the cell, we estimated the cytoplasmic contribution by further processing the images with a median filter (of kernel 6 pixels, corresponding to 0.6 µm).
A 3-by-3 kernel median filter was applied to the data post-acquisition.
This was done by applying an 11 × 11 pixel (1463 × 1463 nm) kernel median filter and dividing the original raw image by the filtered image data.
A median filter with 9 × 9 kernel coefficients is applied to the cover image to generate a filtered image.
Analysis of vesicle co-localisation (Figs. 4 and 7): 3D image stacks were background-subtracted and median filtered (0.5 pixel kernel).
Analysis of NMJ volumes and areas in vivo (Fig. 6): Upon background subtraction BGT-AF555 and BGT-AF647 signals were median filtered (0.5 pixel kernel) and thresholded from 50 255.
Analysis of NMJ size on slices (Fig. 1): Image stacks of BGT-AF647 signals were median filtered (1 pixel kernel) and thresholded from 50 255 (8-bit data) or 600 4095 (12-bit data) greyscale values.
Analysis of NMJ areas and fibre diameters in vivo (Fig. 5): Upon background subtraction BGT-AF647 signals were median filtered (0.5 pixel kernel), maximum z-projections made and these then thresholded from 50 255.
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