Fig 1. Layer structure of the used CNN.

The input layer accepts cell images of 90 × 90px². Avoiding border effects (top and bottom) caused by irregular light refraction at the channel edges, input images are weighted by a Tukey window (α = 0.25) causing a fading effect towards the upper and lower edge (Eq 3). In a first processing stage, images are convoluted by 25 different convolution kernels of size 21 × 21. This results in 25 intermediate images of size 70 × 70px², which undergo a non-linear rectification (reLU layer) before getting down-sampled by a max-pooling procedure (2 × 2, stride 2) to a size of 35 × 35px². The combination of convolution, rectification, and max-pooling are repeated twice using different sets of convolution kernels (see Table 1). The output node then intertwines all resulting subimages by a full interconnection of all available pixel values and maps them to a linear output range. Sizes of subimages (blue/grey) as well as the indications of convolution kernels (black) are chosen to scale, illustrating that kernels obey the characteristic features of input and subimages.