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Optic disc and cup segmentation play a crucial role in automating thescreening and diagnosis of optic glaucoma. While data-driven convolutionalneural networks (CNNs) show promise in this area, the inherent ambiguity ofsegmenting object and background boundaries in the task of optic disc and cupsegmentation leads to noisy annotations that impact model performance. Toaddress this, we propose an innovative label-denoising method of MultiplePseudo-labels Noise-aware Network (MPNN) for accurate optic disc and cupsegmentation. Specifically, the Multiple Pseudo-labels Generation and GuidedDenoising (MPGGD) module generates pseudo-labels by multiple differentinitialization networks trained on true labels, and the pixel-level consensusinformation extracted from these pseudo-labels guides to differentiate cleanpixels from noisy pixels. The training framework of the MPNN is constructed bya teacher-student architecture to learn segmentation from clean pixels andnoisy pixels. Particularly, such a framework adeptly leverages (i) reliable andfundamental insights from clean pixels and (ii) the supplementary knowledgewithin noisy pixels via multiple perturbation-based unsupervised consistency.Compared to other label-denoising methods, comprehensive experimental resultson the RIGA dataset demonstrate our method's excellent performance andsignificant denoising ability.

Accurate Segmentation of Optic Disc And Cup from Multiple Pseudo-labels by Noise-Aware Learning