KiPA22 — Kidney Parsing 2022 Challenge (re-mirror)

Re-hosted mirror of the KiPA22 challenge training set (He et al., 2021, Med. Image Anal.), originally released through the KiPA22 Grand Challenge. This mirror rebuilds the data from the freely-fetchable YongchengYAO/KiPA22 HF dataset (which redistributes under CC-BY-NC-4.0), restructured into the same layout we use for KiTS23 / AbdomenCT1K / etc. so a single Base3DDataset subclass can load it.

Composition

Split	Cases	With mask
train	70	yes

The 30-case open test and 30-case closed test from the original challenge are held out by the organizers and not included here. Use the 70-case train pool with your own splits.

File layout

dataset/case_00000/
  imaging.nii.gz
  segmentation.nii.gz
...
dataset/case_00069/
train.jsonl
README.md

train.jsonl lists one entry per case with image, mask, label, modality, dataset, official_split, patient_id keys. Image/mask paths are prefixed with data/nii/KiPA22/ so they slot directly into the EasyMedSeg Base3DDataset HF_JSONL_PREFIX convention.

Mask labels

CT integer labels:

Value	Class
0	background
1	renal vein
2	kidney
3	renal artery
4	kidney tumor

For binary kidney evaluation, EasyMedSeg's utils.data_preprocess.KiPA22 merges class 4 (tumor) into class 2 (kidney) at preprocessing time, leaving 4 channels [vein, kidney+tumor, artery, tumor].

License

CC-BY-NC-4.0, inherited from the upstream YongchengYAO/KiPA22 mirror. The official challenge organizers' terms (research-only) apply on top. Cite the canonical paper:

@article{he2021meta,
  title   = {Meta grayscale adaptive network for 3D integrated renal structures segmentation},
  author  = {He, Yuting and Yang, Guanyu and Yang, Jian and Ge, Rongjun
             and Kong, Youyong and Zhu, Xiaomei and Zhang, Shaobo
             and Shao, Pengfei and Shu, Huazhong and Dillenseger, Jean-Louis
             and others},
  journal = {Medical Image Analysis},
  volume  = {71},
  pages   = {102055},
  year    = {2021},
  publisher = {Elsevier}
}