{
  "abstract": "BackgroundWhole-brain segmentation of neonatal low-field MRI is needed for regional volumetric analysis, but established segmentation systems are designed around different field strengths, contrasts, and age ranges.\n\nMethodsWe first applied complete end-to-end pipelines, including FreeSurfer, FSL-based processing, Infant FreeSurfer, FastSurfer, BrainSuite, SPM12/CAT12, iBEAT v2, the dHCP structural pipeline, and M-CRIB-S, directly to 0.35T neonatal T2-weighted images. Because those systems did not produce a complete 47-region output, we next tested released end-to-end AI segmentation models, including SynthSeg, FastSurfer, FastSurferVINN, and QuickNAT. These were highly sensitive to input format, contrast, voxel geometry, and the high-field training domain. We then decomposed the task into skull stripping, registration, super-resolution, atlas propagation, and tissue refinement and tested the available components individually.\n\nResultsNo complete end-to-end pipeline or released AI model delivered a usable 47-region segmentation in direct application. Stage-wise testing showed that preprocessing, reconstruction, registration, atlas propagation, and expectation-maximization each solved only part of the problem. Their final assembly produced complete 47-region outputs across the neonatal cohort.\n\nConclusionsDirect application of established pipelines was insufficient for 0.35T neonatal T2-weighted MRI. A stage-wise combination of compatible components provided the workable solution.",
  "abstract_shown": true,
  "affiliations": [
    "Children's Hospital, Zhejiang University School of Medicine"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Z. Sun"
    },
    {
      "name": "J. Huang"
    },
    {
      "name": "X.-N. Zuo"
    },
    {
      "name": "H. He"
    },
    {
      "affs": [
        0
      ],
      "name": "G. Yu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0vawas2nfz67g.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.23.26363741",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.23.26363741v1.full",
  "id": "0vawas2nfz67g",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.09.23.26363741v1",
  "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",
      "1707|Medical Image Segmentation Techniques"
    ]
  ],
  "openalex": "W7214798486",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.23.26363741v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0vawas2nfz67g.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:41Z",
      "native_id": "10.64898/2026.09.23.26363741",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.23.26363741v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:radiology and imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Construction of a whole-brain segmentation pipeline for 0.35T neonatal MRI",
  "title_text": "Construction of a whole-brain segmentation pipeline for 0.35T neonatal MRI",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:41Z",
  "url": "https://research.draconitic.com/paper/0vawas2nfz67g",
  "venue": "medRxiv",
  "versions": []
}