Metraipolis › archive › dc_fca6325666bd

Annotating anatomy and pathology in the National Lung Screening Trial computed tomography images — arXiv:2609.10858

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.10858
License (as archived): CC0-1.0
Archived: 2026-10-16T07:21:42.843Z
SHA-256 of the archived text: 1590abf2afa7ba6dc79a91a22da2193013ac203401cf1bbb86d38f99f7663139
Stable URL of this page: https://metraipolis.com/documento/dc_fca6325666bd

How to cite

Annotating anatomy and pathology in the National Lung Screening Trial computed tomography images — arXiv:2609.10858 — arXiv (en), https://arxiv.org/abs/2609.10858 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fca6325666bd (Metraipolis archive dc_fca6325666bd, sha256 1590abf2afa7ba6dc79a91a22da2193013ac203401cf1bbb86d38f99f7663139)

BibTeX

@misc{dcfca6325666bd,
  title = {Annotating anatomy and pathology in the National Lung Screening Trial computed tomography images — arXiv:2609.10858},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.10858},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fca6325666bd; sha256 1590abf2afa7ba6dc79a91a22da2193013ac203401cf1bbb86d38f99f7663139},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.10858. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Large-scale public medical imaging datasets contribute critically to translational research. When accompanied by rich clinical and multi-omics data, they can stimulate exploratory research and enable secondary analyses. Expert annotations of such imaging collections can support the development of new image analysis tools. Continuous enrichment of images with image-derived data makes them more usable for researchers without expertise in image analysis or access to large-scale computational resources. The National Lung Screening Trial (NLST) released a rich longitudinal dataset that includes Computed Tomography (CT) images for over 26,000 patients. We introduce three Digital Imaging and Communications in Medicine (DICOM) formatted datasets, complementing NLST CT images, shared as analysis results in the National Cancer Institute Imaging Data Commons (IDC). Two of those (IDC NLSTSeg and IDC NLSTSybil) contain DICOM-harmonized annotations and extracted measurements (for 581 and 601 NLST patients, respectively) shared earlier using research formats (Sybil and NLSTseg). The third one (TotalSegmentator-CT-Segmentations) contains volumetric segmentations generated using TotalSegmentator and radiomics features for each segment for 26,194 NLST patients.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-11 in eess.IV).
Categorie arXiv: Image and Video Processing (eess.IV).
Pagina del lavoro: https://arxiv.org/abs/2609.10858 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T06:59:40.720Z dalla pagina https://arxiv.org/catchup/eess.IV/2026-09-11?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).