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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 theiterative refinement. The model was mainly applied to achieve higherrecognition 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 andthe state-of-the-art methods for the investigated tasks, very high segmentationquality has been achieved without a need for increasing the volume of datasets.Our experiments include comparison with the best-known methods on publiclyavailable databases DRIONS-DB, RIM-ONE v.3, DRISHTI-GS, and evaluation on aprivate data set collected in collaboration with University of California SanFrancisco Medical School. The analysis of the architecture details ispresented, and it is argued that the model can be employed for a broad scope ofimage segmentation problems of similar nature.

Stack-U-Net: Refinement Network for Image Segmentation on the Example of Optic Disc and Cup