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KiTS23: 489 kidney-tumour CTs with kidney, tumour and cyst labels

pl.load("hf_kits23") loads this dataset. The 2023 Kidney and Kidney Tumor Segmentation challenge imaging as published by the organisers on the Hub: contrast-enhanced abdominal CT per case (100-280 MB) with a three-class label map. Two scoped folders of the repo are listed (images, segmentations).

import pardesline as pl

ds = pl.load("hf_kits23")
print(ds.describe())  # roles, frames, units, labels, licence, citation
s = ds[0]
obj = s.ct  # fetched now, cached under ~/.cache/pardesline/datasets/hf_kits23

Provenance and licence
Source Hugging Face Hub repo neheller/KiTS-Challenge-Imaging, paths images, segmentations
Scheme hf
Size of the whole source 87.9 GB
Licence CC-BY-NC-SA-4.0
Commercial use not allowed (non-commercial licence)
Redistribution allowed
Licence note CC BY-NC-SA 4.0 on the Hub card (checked 2026-10-14): non-commercial, share-alike.
Terms https://kits-challenge.org/kits23/
Homepage https://kits-challenge.org/kits23/
Cite Heller N., Isensee F., Trofimova D., et al. The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT. arXiv:2307.01984, 2023.
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 images/{id}.nii.gz
Abdominal CT (NIfTI)
mask labels from the file header from the file header 1=kidney, 2=tumor, 3=cyst segmentations/{id}.nii.gz
Kidney / tumour / cyst label map

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("hf_kits23").take(2)
ds.export("./standardized")        # PLY / MetaImage + sidecars + manifest.json

Algorithms and models that run on it

Generated from src/pardesline/data/manifests/hf_kits23.json on 2026-10-14.