Joint Optic Disc and Cup Segmentation with Parallel Cooperative Diffusion Model

Glaucoma is a common eye disease that can lead to permanent vision loss. Accurate segmentation of the optic disc (OD) and optic cup (OC) plays a crucial role in glaucoma screening and assessment. However, existing methods predominantly focus on separate segmentation of the OD and OC, neglecting the interrelationship between the two structures. Furthermore, due to the optic cup’s indistinct boundary, most existing methods fail to generate accurate OC region segmentation from fundus images, resulting in errors in cup-to-disc ratio (CDR) measurements. Therefore, we propose a parallel cooperative diffusion model, to improve the segmentation accuracy through joint segmentation of OD and OC. This is the first application of diffusion models in the joint segmentation of the OD and OC. We integrate a denoising autoencoder with coupling function into the diffusion model to enhance its performance and generalization ability. Additionally, we proposed a joint U-Net to simultaneously learn the joint distribution and semantic correlation of the OD and OC, thereby improving the segmentation accuracy and robustness of the results. Furthermore, we introduce a cross-attention block to bridge the two subnets of the OD and OC, achieving alignment of the segmentation results. The proposed model is evaluated on publicly available datasets including DrishtiGS, RIM-ONE (r3), and REFUGE. Experimental results demonstrate that our model outperforms state-of-the-art segmentation methods in OC and OD segmentation performance.