LUNA16

A mirror of the LUNA16 (LUng Nodule Analysis 2016) challenge data — 888 thoracic LDCT scans derived from LIDC-IDRI — repackaged for use in the EasyMedSeg medical segmentation framework.

Contents

• subset0/ … subset9/ — 888 CT scans in MetaImage format (.mhd + .raw)
• seg-lungs-LUNA16/ — official lung-field masks (.mhd + .zraw)
• nodule-masks-spheres/ — derived nodule masks built from annotations.csv by drawing a sphere of diameter_mm at each nodule centroid.
• annotations.csv — 1,186 nodule annotations (centroid + diameter)
• candidates.csv, candidates_V2.csv — false-positive-reduction candidates
• sampleSubmission.csv — challenge submission template

Mask sources

LUNA16 ships only centroid+diameter annotations for nodules — there are no official voxel-level nodule masks. Two derived mask sources are provided here:

1. Lung-field masks (seg-lungs-LUNA16/) — official, paired 1:1 with all 888 scans. Per the original release: "provided to aid nodule detection; NOT intended as the reference standard for any segmentation study."
2. Nodule sphere masks (nodule-masks-spheres/) — generated by the uploader from annotations.csv: for each scan, a binary uint8 volume with a sphere of diameter_mm placed at each nodule centroid. Use as an approximate target for nodule segmentation; not a true voxel-level GT.

Source

• Zenodo Part 1: https://zenodo.org/records/3723295 (subsets 0–6, masks, annotations)
• Zenodo Part 2: https://zenodo.org/records/4121926 (subsets 7–9)
• Challenge homepage: https://luna16.grand-challenge.org/

License

CC BY 4.0 — same as the original Zenodo release.

Citation

Setio, A.A.A., Traverso, A., de Bel, T., Berens, M.S.N., et al. (2017). Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge. Medical Image Analysis 42: 1–13. doi:10.1016/j.media.2017.06.015