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Existing joint optic disc and cup segmentation approaches are developedeither in Cartesian or polar coordinate system. However, due to the subtleoptic cup, the contextual information exploited from the single domain even bythe prevailing CNNs is still insufficient. In this paper, we propose a novelsegmentation approach, named Cartesian-polar dual-domain network (DDNet), whichfor the first time considers the complementary of the Cartesian domain and thepolar domain. We propose a two-branch of domain feature encoder and learntranslation equivariant representations on rectilinear grid from Cartesiandomain and rotation equivariant representations on polar grid from polar domainparallelly. To fuse the features on two different grids, we propose adual-domain fusion module. This module builds the correspondence between twogrids by the differentiable polar transform layer and learns the featureimportance across two domains in element-wise to enhance the expressivecapability. Finally, the decoder aggregates the fused features from low-levelto high-level and makes dense predictions. We validate the state-of-the-artsegmentation performances of our DDNet on the public dataset ORIGA. Accordingto the segmentation masks, we estimate the commonly used clinical measure forglaucoma, i.e., the vertical cup-to-disc ratio. The low cup-to-disc ratioestimation error demonstrates the potential application in glaucoma screening.

DDNet: Cartesian-polar Dual-domain Network for the Joint Optic Disc and Cup Segmentation