{
  "abstract": "Automated segmentation of Optical Coherence Tomography (OCT) images is a critical component of structural biomarker extraction for retinal diagnostics. Deep learning models achieve state-of-the-art performance on controlled datasets, yet exhibit unpredictable failures on real-world data. Current quality gates rely on device-reported scan quality scores, which have been shown to be unreliable predictors of segmentation performance. We define scan quality in a task-specific sense that is, whether a given B-scan will yield a reliable segmentation from a particular trained model. Under this definition, we perform a systematic evaluation of No-Reference Image Quality Assessment (NR-IQA) metrics, general-purpose and domain-specific pretrained representations as alternative quality gates. To this end, we use choroid segmentation as the prototype task, with a dataset of 6,076 OCT B-scans from 80 subjects. These quality gate candidates are evaluated at three levels: scalar metrics (BRISQUE, NIQE, PIQE, SNR, PSNR), supervised linear probing, and unsupervised partitioning (K-Means) of the feature vectors and the learned representations. All scalar NR-IQA metrics proved inadequate (|r| < 0.20). General-purpose ImageNet-based pretrained representations (EfficientNet-b0, ResNet-50, ViT-B/16) improve upon NR-IQA, achieving ROC-AUC up to 0.77, indicating that learned representations are better suited to task-specific quality gating than hand-crafted scalar statistics. Retinal foundation models (FMs) further close the gap: RETFound (OCT-specific FM) achieves ROC-AUC {approx} 0.81. Unsupervised K-Means partitioning indicates that general-purpose ImageNet-pretrained embeddings, despite carrying a linearly decodable quality signal, do not reliably organize scans by quality geometrically, whereas the retinal FMs produce quality-aligned clusters that exceed a patient-level permutation null, suggesting that domain-specific pretraining provides additional, complementary benefit on top of general-purpose learned representations.",
  "abstract_shown": true,
  "affiliations": [
    "University of Pittsburgh"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "name": "A. Hiras"
    },
    {
      "affs": [
        0
      ],
      "name": "A. Gadari"
    },
    {
      "name": "A. Jayaraman"
    },
    {
      "name": "A. S. Mankumare"
    },
    {
      "name": "A. Mynampati"
    },
    {
      "name": "J. K. Chhablani"
    },
    {
      "name": "S. C. Bollepalli"
    },
    {
      "name": "K. K. Vupparaboina"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1g374h3xxp6r2.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.14.26363079",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.14.26363079v1.full",
  "id": "1g374h3xxp6r2",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.09.14.26363079v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "ophthalmology"
    ],
    [
      "openalex",
      "2741|Retinal Imaging and Analysis"
    ]
  ],
  "openalex": "W7213255537",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.14.26363079v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1g374h3xxp6r2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:39Z",
      "native_id": "10.64898/2026.09.14.26363079",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.14.26363079v2"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Retinal Imaging and Analysis",
      "subfield": "imaging"
    }
  ],
  "title": "Task-Specific Quality Gating for Retinal Optical Coherence Tomography B-Scans: Learned Representations Over Scalar Metrics in Choroid Segmentation",
  "title_text": "Task-Specific Quality Gating for Retinal Optical Coherence Tomography B-Scans: Learned Representations Over Scalar Metrics in Choroid Segmentation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:42Z",
  "url": "https://research.draconitic.com/paper/1g374h3xxp6r2",
  "venue": "medRxiv",
  "versions": []
}