{
  "abstract": "Background: Glaucoma is an irreversible blinding disease for which early detection is essential. Precise segmentation of the optic disc (OD) and optic cup (OC) enables accurate cup-to-disc ratio (CDR) calculation, which serves as an important reference for glaucoma evaluation. However, the ambiguous boundaries between these structures make manual segmentation time-consuming and highly subjective.\n\nMethods: This study proposes a hybrid framework for OD and OC segmentation in fundus images. The framework first adopts YOLOv10 to achieve coarse localization and extraction of the OD region, followed by fine-grained segmentation using the proposed EEGA-VMUNet network. Based on a Vision Mamba-UNet backbone, we design an enhanced edge-guided attention (EEGA) module to adaptively integrate multi-scale contextual information. This module strengthens the representation of edges and fine structural features. A multi-scale loss function is also used to improve robustness to scale variations.\n\nResults: Experiments on four benchmark datasets demonstrate that EEGA-VMUNet achieves competitive segmentation performance. On the same-domain test sets, OD Dice coefficients range from 95.20% to 97.65% and OC Dice coefficients range from 87.51% to 89.95%. Zero-shot cross-domain experiments achieve an OD Dice of 95.18% and an OC Dice of 85.13%.\n\nConclusion: The proposed model achieves competitive performance in both OD and OC segmentation tasks. Bland-Altman analysis suggests that the derived vertical cup-to-disc ratio (VCDR) holds potential as a diagnostic aid for glaucoma screening, although its precision may require further improvement for longitudinal monitoring.\n\nSupplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12886-026-05371-x.",
  "abstract_shown": true,
  "affiliations": [
    "School of Medicine and Health, Guangzhou Xinhua University, Guangzhou, 510520 China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Lili Cai"
    },
    {
      "affs": [
        0
      ],
      "name": "Keke Hou",
      "orcid": "0000-0001-8497-837X"
    },
    {
      "affs": [
        0
      ],
      "name": "Zhong Mo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/78pdsbz6d50ne.bib",
  "cited_by": 0,
  "doi": "10.1186/s12886-026-05371-x",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13711713/",
  "id": "78pdsbz6d50ne",
  "issns": [
    "1471-2415"
  ],
  "landing_url": "https://doi.org/10.1186/s12886-026-05371-x",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Research"
    ],
    [
      "openalex",
      "2741|Retinal Imaging and Analysis"
    ]
  ],
  "openalex": "W7223316712",
  "pdf_url": null,
  "pmcid": "PMC13711713",
  "pmid": "42947045",
  "published": "2026-10-23",
  "publisher": "BMC",
  "ris": "https://research.draconitic.com/paper/78pdsbz6d50ne.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T12:22:26Z",
      "native_id": "PMC13711713",
      "source": "pmc",
      "url": "https://doi.org/10.1186/s12886-026-05371-x"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Retinal Imaging and Analysis",
      "subfield": "imaging"
    }
  ],
  "title": "Optic disc and cup segmentation using Vision Mamba UNet with enhanced edge-guided attention in fundus images",
  "title_text": "Optic disc and cup segmentation using Vision Mamba UNet with enhanced edge-guided attention in fundus images",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T12:22:26Z",
  "url": "https://research.draconitic.com/paper/78pdsbz6d50ne",
  "venue": "BMC Ophthalmology",
  "versions": []
}