  In this work, we propose a special cascade network for image segmentation,
which is based on the U-Net networks as building blocks and the idea of the
iterative refinement. The model was mainly applied to achieve higher
recognition quality for the task of finding borders of the optic disc and cup,
which are relevant to the presence of glaucoma. Compared to a single U-Net and
the state-of-the-art methods for the investigated tasks, very high segmentation
quality has been achieved without a need for increasing the volume of datasets.
Our experiments include comparison with the best-known methods on publicly
available databases DRIONS-DB, RIM-ONE v.3, DRISHTI-GS, and evaluation on a
private data set collected in collaboration with University of California San
Francisco Medical School. The analysis of the architecture details is
presented, and it is argued that the model can be employed for a broad scope of
image segmentation problems of similar nature.
