  This thesis studies how the segmentation results, produced by convolutional
neural networks (CNN), is different from each other when applied to small
biomedical datasets. We use different architectures, parameters and
hyper-parameters, trying to find out the better configurations for our task,
and trying to find out underlying regularities. Two working datasets are from
biomedical area of research. We conducted a lot of experiments with the two
types of networks and the received results have shown the preference of some
conditions of experiments and parameters of the networks over the others. All
testing results are given in the tables and some selected resulting graphs and
segmentation predictions are shown for better illustration.
