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TCIA LIDC-IDRI: 1 018 thoracic CTs for lung nodule analysis (DICOM)

pl.load("tcia_lidc_idri") loads this dataset. The Lung Image Database Consortium collection: 1 018 CT scans of 1 010 patients with nodule annotations by four radiologists (the XML annotations are on the collection page; DICOM SEG and SR objects derived from them are in NBIA as separate series). This manifest exposes the CT series; patients with several CT series keep the first. Series are listed through the public NBIA API (no account) and each one is downloaded as a zip of DICOM files on first access; the folder is then a DICOM volume (LPS mm from the headers).

import pardesline as pl

ds = pl.load("tcia_lidc_idri")
print(ds.describe())  # roles, frames, units, labels, licence, citation
s = ds[0]
obj = s.ct  # fetched now, cached under ~/.cache/pardesline/datasets/tcia_lidc_idri

Provenance and licence
Source The Cancer Imaging Archive, collection LIDC-IDRI through the NBIA REST API
Scheme tcia
Size of the whole source 122.1 GB
Licence CC-BY-3.0
Commercial use allowed by the licence
Redistribution allowed
Licence note CC BY 3.0 on the TCIA collection page (checked 2026-10-14).
Terms https://www.cancerimagingarchive.net/collection/lidc-idri/
Homepage https://www.cancerimagingarchive.net/collection/lidc-idri/
Cite Armato S.G. III, McLennan G., Bidaut L., et al. Data From LIDC-IDRI. The Cancer Imaging Archive, 2015. doi:10.7937/K9/TCIA.2015.LO9QL9SX. Clark K., Vendt B., Smith K., et al. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. J Digit Imaging 26(6):1045-1057, 2013.
Samples grouped by id

Roles (what each sample contains)

Role Type Frame Units Labels / layout Pattern or file
ct volume (CT) from the file header from the file header {id}/CT/{series}
Thorax CT, DICOM series
seg file from the file header from the file header {id}/SEG/{series}
DICOM SEG object derived from the annotations (one of several per patient)

Standardized layout

Files are fetched on first access into the dataset cache and stay in their original format; ds.export(folder) writes every sample as standard files (PLY for points and meshes, MetaImage for volumes and label maps) with a .pardesline.json sidecar carrying frame, units, labels, licence and provenance, plus a manifest for the folder. Label maps become exact surfaces with to_mesh(label=k), DICOM series become one volume, raw arrays are decoded through the layout above.

ds = pl.load("tcia_lidc_idri").take(2)
ds.export("./standardized")        # PLY / MetaImage + sidecars + manifest.json

Algorithms and models that run on it

Generated from src/pardesline/data/manifests/tcia_lidc_idri.json on 2026-10-14.