  Deep neural networks show high accuracy in theproblem of semantic and
instance segmentation of biomedicaldata. However, this approach is
computationally expensive. Thecomputational cost may be reduced with network
simplificationafter training or choosing the proper architecture, which
providessegmentation with less accuracy but does it much faster. In thepresent
study, we analyzed the accuracy and performance ofUNet and ENet architectures
for the problem of semantic imagesegmentation. In addition, we investigated the
ENet architecture by replacing of some convolution layers with
box-convolutionlayers. The analysis performed on the original dataset consisted
of histology slices with mast cells. These cells provide a region
forsegmentation with different types of borders, which vary fromclearly visible
to ragged. ENet was less accurate than UNet byonly about 1-2%, but ENet
performance was 8-15 times faster than UNet one.
