  Solving segmentation tasks with topological priors proved to make fewer
errors in fine-scale structures. In this work, we use topological priors both
before and during the deep neural network training procedure. We compared the
results of the two approaches with simple segmentation on various accuracy
metrics and the Betti number error, which is directly related to topological
correctness, and discovered that incorporating topological information into the
classical UNet model performed significantly better. We conducted experiments
on the ISBI EM segmentation dataset.
