{
  "abstract": "Manual localization of anatomical landmarks within human cochlea structures in 3D computed tomography (CT) images is labor-intensive. However, despite progress in various studies on automatic localization methods, it remains challenging to jointly achieve rapid, precise, and fully automated landmark localization. Therefore, we propose a deep learning-based localization framework, called MEDICO-YOLO, which bridges speed and precision. It can automatically and simultaneously localize 6 human cochlea landmarks (apex, basal point, round window center of both left and right cochleae) in the full-resolution 3D CT images. The main contributions are: 1) MEDICO-YOLO learns from few manually annotated 3D CT images; 2) Within the framework, a 2D CNN-based network was developed to specifically localize cochlea landmarks in 3D single-scale medical CT images; 3) A novel loss function was designed; 4) The localization model achieved human expert-level precision while ensuring rapid inference. This study employed anonymized 3D pre-operative CT images (0.3 mm, covering the full head or temporal bone region). The evaluation involved comparing four models that were trained using 4 different training set sizes (n=4, 8, 12, 16). Each of them was evaluated on a distinct test set (n=16). The results show that the MEDICO-YOLO framework achieved strong localization performance by learning from few annotated 3D CT images. Trained on only 8 CT volumes, the model already attained a mean localization error of 1.30 mm (approximately 4 voxels). When trained on 16 CT volumes, the model achieved a mean error of 0.71 mm and a successful detection rate (SDR@3.0 mm) of 97.92%. Furthermore, on an Apple M1 laptop, the model achieved a real-time speed (32 ms) for inferencing each 2D slice (608 x 608) and an average latency of 25.43 s for inferencing the full 3D CT image (608 x 608 x 200, including post-processing). The complete methodology for the MEDICO-YOLO framework is presented in this article.",
  "abstract_shown": true,
  "affiliations": [
    "Hannover Medical School"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Y. Wang"
    },
    {
      "name": "A. Kral"
    },
    {
      "name": "T. Lenarz"
    },
    {
      "name": "S. John"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6aqbtgr7c18s8.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.11.06.26366894",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.11.06.26366894v1.full",
  "id": "6aqbtgr7c18s8",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.11.06.26366894v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "radiology and imaging"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.11.06.26366894v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6aqbtgr7c18s8.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T00:20:54Z",
      "native_id": "10.64898/2026.11.06.26366894",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.11.06.26366894v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:radiology and imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Deep Learning-based Framework for Rapid, Precise and Fully Automated Localization of Cochlea Landmarks in 3D CT Images",
  "title_text": "Deep Learning-based Framework for Rapid, Precise and Fully Automated Localization of Cochlea Landmarks in 3D CT Images",
  "type": "preprint",
  "updated_at": "2026-11-09T12:20:30Z",
  "url": "https://research.draconitic.com/paper/6aqbtgr7c18s8",
  "venue": "medRxiv",
  "versions": []
}