{
  "abstract": "Purpose: To develop and validate a deep learning-based method for the automated identification and volumetric segmentation of microaneurysms (MAs) in diabetic retinopathy (DR) using OCT. Participants: A total of 125 participants were enrolled, including 20 healthy eyes, 27 with mild NPDR, 30 with moderate NPDR, 30 with severe NPDR, and 18 with PDR. Methods: We obtained multiple repeated 3x3-mm scans from each participant using a commercial 120-kHz spectral-domain OCT system (Solix; Visionix/Optovue, Inc., California, USA), which were registered and averaged to generate high-definition volumes. We developed a 3D deep learning network to segment MAs volumetrically. The input to the network consists of the original OCT volume concatenated with its reflectance-inverted counterpart. Expert graders manually delineated MAs to generate annotations. We evaluated model performance at multiple levels, including voxel-level segmentation, lesion-level detection, and eye-level diagnosis. Results: In the test dataset (20 healthy, 20 DR eyes), the algorithm demonstrated high voxel-level accuracy, with F1 scores of 79.2% on single volumes and 86.1% on averaged volumes. Lesion-level detection reached an F1 score of 96.0% (single) and 97.1% (averaged), while scan-level diagnostic accuracy for MA presence was 97.5% (single and averaged). Conclusions: A deep learning-based method can accurately identify and segment MAs volumetrically on OCT, enabling quantification and characterization, and potentially aiding in the diagnosis, monitoring, and management of DR.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.13508",
  "author_total": 8,
  "authors": [
    {
      "name": "Min Gao"
    },
    {
      "name": "Yukun Guo"
    },
    {
      "name": "Tristan T. Hormel"
    },
    {
      "name": "Jinyi Hao"
    },
    {
      "name": "Azaz Khan"
    },
    {
      "name": "Steven T. Bailey"
    },
    {
      "name": "Thomas S. Hwang"
    },
    {
      "name": "Yali Jia"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6g4tqy7e9gyeh.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.13508",
  "fulltext_url": null,
  "id": "6g4tqy7e9gyeh",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.13508",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "q-bio.QM"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.13508",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-11",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6g4tqy7e9gyeh.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:30Z",
      "native_id": "2609.13508",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.13508"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Automated Volumetric Segmentation of Microaneurysms on OCT Using Artificial Intelligence",
  "title_text": "Automated Volumetric Segmentation of Microaneurysms on OCT Using Artificial Intelligence",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:30Z",
  "url": "https://research.draconitic.com/paper/6g4tqy7e9gyeh",
  "venue": "arXiv",
  "versions": []
}