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Dataset

Part of the dataset is adapted from MSD ( Liver, Spleen, Pancreas ), NIH Pancreas, and KiTS under their license permission. We also include a hidden testing set with 50 abdomen CT cases from Nanjing University. Annotations include four organs: liver ( label=1 ), kidney ( label=2 ), spleen ( label=3 ), and pancreas ( label=4 ). More details are in the following paper:

@article{AbdomenCT-1K,
title={AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?},
author={Ma, Jun and Zhang, Yao and Gu, Song and Zhu, Cheng and Ge, Cheng and Zhang, Yichi and An, Xingle and Wang, Congcong and Wang, Qiyuan and Liu, Xin and Cao, Shucheng and Zhang, Qi and Liu, Shangqing and Wang, Yunpeng and Li, Yuhui and He, Jian and Yang, Xiaoping},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2021},
doi={10.1109/TPAMI.2021.3100536}
}

The testing set is held by the organizers with the following folder structure:

inputs/ ├── test_001_0000.nii.gz ├── test_002_0000.nii.gz  ... ├── test_100_0000.nii.gz The segmentation results should be   outputs/ ├── test_001.nii.gz ├── test_002.nii.gz  ... ├── test_100.nii.gz

Download Training and Validation Set

(License: CC-BY-NC-SA)

Zenodo 

BaiduNetDisk (PW:2021)

Baseline

FLARE21 nnUNet Baseline

Reference

[1] Simpson, Amber L., Michela Antonelli, Spyridon Bakas, Michel Bilello, Keyvan Farahani, Bram van Ginneken, Annette Kopp-Schneider, Bennett A. Landman, Geert Litjens, and Bjoern H. Menze. 2019. “A Large Annotated Medical Image Dataset for the Development and Evaluation of Segmentation Algorithms.” ArXiv Preprint ArXiv:1902.09063.

[2] Heller, Nicholas, Niranjan Sathianathen, Arveen Kalapara, Edward Walczak, Keenan Moore, Heather Kaluzniak, Joel Rosenberg et al. "The kits19 challenge data: 300 kidney tumor cases with clinical context, ct semantic segmentations, and surgical outcomes." arXiv preprint arXiv:1904.00445 (2019).

[3] Holger R. Roth, Amal Farag, Evrim B. Turkbey, Le Lu, Jiamin Liu, and Ronald M. Summers. (2016). Data From Pancreas-CT. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU.

[4] Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, 2013, pp 1045-1057. DOI: https://doi.org/10.1007/s10278-013-9622-7.