{
  "abstract": "Accurate segmentation of dental caries in intraoral periapical radiographs remains challenging because lesions are often small, low-contrast, and visually confounded by surrounding anatomical structures. To address this problem, an ROI-based binary caries segmentation framework is proposed using a ResNet conditioned U-Net with a FiLM-modulated decoder. The method combines a standard 4-level U-Net segmentation stream with a lightweight TinyResNetContext branch that extracts a global representation from the same ROI input. This context vector is used to generate feature-wise modulation parameters that adapt decoder feature refinement at multiple stages, enabling case-specific lesion delineation. The study was conducted on a dataset of 1,887 intraoral periapical radiographs collected from SIBAR Institute of Dental Sciences, for which binary lesion masks were generated through CVAT-based annotation. Following preprocessing, dataset partitioning, and training-time augmentation, the proposed model was evaluated against U-Net, U-Net++, and ResNet34 baselines under both ROI-based and non-ROI settings using an identical training and evaluation protocol. Experimental results showed that ROI-based training consistently outperformed full-image segmentation across all baseline families, confirming the importance of lesion-focused inputs. Among all evaluated models, the proposed method achieved the strongest overall test-set performance, with Precision = 0.8302, Recall = 0.9049, F1-score = 0.8659, Dice = 0.8659, IoU = 0.7635, and AP50 = 0.9792. Checkpoint-level statistical analysis further demonstrated significant improvements over U-Net (ROI) and all non-ROI baselines, while also indicating favorable generalization relative to competing ROI-based models. In addition, a multi-seed robustness analysis on ROI-based models confirmed that the observed performance gains were reproducible across different random initializations. These findings suggest that integrating global ROI context into decoder refinement provides a competitive and computationally lightweight strategy for binary caries lesion segmentation under predefined ROI-based radiographic analysis settings. The present framework should be interpreted as an ROI-based segmentation approach rather than a fully end-to-end automated clinical caries detection and segmentation workflow operating directly on uncropped radiographs.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "D. Meghana"
    },
    {
      "name": "A. Manimaran"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b4sxjj4pstrwq.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-71285-9",
  "fulltext_url": null,
  "id": "b4sxjj4pstrwq",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-71285-9",
  "language": null,
  "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": [
    [
      "openalex",
      "3504|Dental Radiography and Imaging"
    ]
  ],
  "openalex": "W7220361804",
  "pdf_url": "https://www.nature.com/articles/s41598-026-71285-9_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/b4sxjj4pstrwq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:22Z",
      "native_id": "10.1038/s41598-026-71285-9",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-71285-9"
    }
  ],
  "subjects": [],
  "title": "ROI-based binary caries segmentation in intraoral periapical radiographs using a ResNet-conditioned U-Net with FiLM-modulated decoder",
  "title_text": "ROI-based binary caries segmentation in intraoral periapical radiographs using a ResNet-conditioned U-Net with FiLM-modulated decoder",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:22Z",
  "url": "https://research.draconitic.com/paper/b4sxjj4pstrwq",
  "venue": "Scientific Reports",
  "versions": []
}