FLARE-MedFM/PancancerCTSeg

MICCAI FLARE Task1 Pan-cancer Segmentation Dataset

Note: Please fill out the registration form on the challenge website below to have your data access request approved.

This is the dataset for MICCAI26 Challenge: Pan-cancer segmentation in CT scans. We have curated over 17,000 labeled cancer CT scans, aiming to promote the development of accurate and robust pan-cancer segmentation models in low-resource settings.

Data Source and Structure

Substantial time and effort were invested by each team of the datasets. Please cite the original dataset papers in your publications.

Disease target	# cases	Source	License	Dataset link
Head and neck cancer (240)	240	SegRap25 [1, 2]	CC BY-SA 4.0	Link
Esophagus cancer (154)	154	AbdomenAtlas [3]	CC BY-NC 4.0	Link
Lung cancer (5854)	4049	LUNA25 [4, 5, 6]	CC BY-NC 4.0	Link
	1010	LIDC-IDRI [7]	CC BY 4.0	Link
	415	NSCLC-Radiomics [8]	CC BY 3.0	Link
	229	LNDb [9]	CC BY 4.0	Link
	88	NSCLC-Radiogenomics [10]	CC BY 3.0	Link
	63	MSD-LungTumor [11]	CC BY-SA 4.0	Link
Liver cancer (1658)	842	TotalSeg-liver_lesions [12]	CC BY 4.0	Link
	303	MSD-HepaticVessel [11]	CC BY-SA 4.0	Link
	219	WAW-TACE [13]	CC BY 4.0	Link
	118	MSD-Liver [11]	CC BY-SA 4.0	Link
	102	Colorectal-Liver-Metastases [14]	CC BY 4.0	Link
	74	HCC-TACE-SEG [15]	CC BY 4.0	Link
Adrenal cancer (52)	52	Adrenal-ACC-Ki67-Seg [16]	CC BY 3.0	Link
Pancreatic cancer (1800)	876	PanTS [17]	CC BY-NC-ND 4.0	Link
	482	PANORAMA [18]	CC BY 4.0	Link
	281	MSD-Pancreas [11]	CC BY-SA 4.0	Link
	161	PanTrack [19, 20]	CC BY-NC-SA 4.0	Link
Kidney cancer (488)	488	KiTS [21]	CC BY-NC-SA 4.0	Link
Lymph nodes (530)	354	Mediastinal-Lymph-Node-SEG [22]	CC BY 4.0	Link
	176	CT-Lymph-Nodes [23]	CC BY 3.0	Link
Colon cancer (126)	126	MSD-Colon [11]	CC BY-SA 4.0	Link
Endometrial cancer (79)	79	AbdomenAtlas [3]	CC BY-NC 4.0	Link
Whole body cancer (6782)	5000	DeepLesion [6, 24]	Open license	Link
	692	autoPETCT [25]	CC BY 3.0	Link
	606	LongitudinalCTLesion [26]	CC BY 4.0	Link
	484	MSWAL [27]	CC BY-NC 4.0	Link

train_label： This dataset aggregates all publicly available CT cancer datasets that include lesion annotations. Please note that it is partially labeled: only the primary lesion is annotated in each case, while other lesions, such as metastases, may remain unlabeled.

train_per_cancer_type： We also provide fine-grained datasets organized by cancer type. Participants may develop and submit cancer-specific models for five major cancer types: lung, liver, pancreatic, kidney, and colon cancer.

train_unlabel The unlabeled datasets may optionally be used for model training. Participants are also encouraged to use other large-scale public datasets, such as CT-RATE and Merlin, for self-supervised pretraining.

validation-public： Both images and annotations are provided. Please do not use them for model training!

validation-hidden： The labels of the hidden validation set will not be released. Please submit the segmentation results on codabench to get the metrics.

PancancerCTSeg/
├── train_label/
│   ├── imagesTr/
│   └── labelsTr/
├── train_per_cancer_type/
│   ├── Dataset001_HeadNeckCancer/
│   │   ├── imagesTr/
│   │   └── labelsTr/
│   ├── Dataset002_EsophagusCancer/
│   ├── Dataset003_LungCancer/
│   ├── Dataset004_LiverCancer/
│   ├── Dataset005_AdrenalCancer/
│   ├── Dataset006_PancreaticCancer/
│   ├── Dataset007_KidneyCancer/
│   ├── Dataset008_LymphNodes/
│   ├── Dataset009_ColonCancer/
│   ├── Dataset010_EndometrialCancer/
│   └── Dataset011_WholeBody/
├── train_unlabel/
│   ├── AMOS-2350/
│   └── MSD-506/
│       ├── MSD-Colon/
│       ├── MSD-HapaticVessel/
│       ├── MSD-Liver/
│       ├── MSD-Lung/
│       ├── MSD-Pancreas/
│       └── MSD-Spleen/
├── validation/
│   ├── HealthyImages-noLesion/
│   ├── Validation-Hidden-Images/
│   ├── Validation-Public-Images/
│   └── Validation-Public-Labels/
└── README.md

Dataset Download Instructions

Directly download the merged dataset:

from huggingface_hub import snapshot_download

local_dir = "./"
snapshot_download(
    repo_id="FLARE-MedFM/PancancerCTSeg",
    repo_type="dataset",
    local_dir=local_dir,
    allow_patterns=["train_label/**"],
)

Directly download the cancer-wise dataset (e.g., liver cancer):

from huggingface_hub import snapshot_download

local_dir = "./"
snapshot_download(
    repo_id="FLARE-MedFM/PancancerCTSeg",
    repo_type="dataset",
    local_dir=local_dir,
    allow_patterns=[
        "train_per_cancer_type/Dataset004_LiverCancer/**"
    ],
)

Directly download the unlabeled dataset:

from huggingface_hub import snapshot_download

local_dir = "./"
snapshot_download(
    repo_id="FLARE-MedFM/PancancerCTSeg",
    repo_type="dataset",
    local_dir=local_dir,
    allow_patterns=["train_unlabel/**"],
)

Directly download the whole dataset (not recommended. It is more data-efficient to download only the merged dataset or a cancer-wise dataset):

from huggingface_hub import snapshot_download

local_dir = "./"
snapshot_download(
    repo_id="FLARE-MedFM/PancancerCTSeg",
    repo_type="dataset",
    local_dir=local_dir,
)

Reference

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• [17] W. Li, X. Zhou, Q. Chen, T. Lin, P. R. A. S. Bassi, X. Chen, C. Ye, Z. Zhu, K. Ding, H. Li, et al., "PanTS: The pancreatic tumor segmentation dataset," in Advances in Neural Information Processing Systems, vol. 38, 2025.

• [18] N. Alves, M. Schuurmans, D. Rutkowski, A. Saha, P. Vendittelli, N. Obuchowski, M. H. Liedenbaum, I. S. Haldorsen, A. Molven, D. Yakar, et al., "Artificial intelligence and radiologists in pancreatic cancer detection using standard of care CT scans (PANORAMA): An international, paired, non-inferiority, confirmatory, observational study," The Lancet Oncology, vol. 27, no. 1, pp. 116–124, 2026.

• [19] Y. Kirchhoff, M. Rokuss, D. P. Mertens, D. Füller, B. Hamm, A. Schreyer, O. Ritter, and K. Maier-Hein, "Exploiting longitudinal context in clinician-verified interactive lesion tracking," arXiv preprint arXiv:2605.23118, 2026.

• [20] M. R. Rokuss, Y. Kirchhoff, S. Roy, B. Kovacs, C. Ulrich, T. Wald, M. Zenk, S. Denner, F. Isensee, P. Vollmuth, J. Kleesiek, and K. Maier-Hein, "Longitudinal segmentation of MS lesions via temporal difference weighting," in Medical Image Computing and Computer Assisted Intervention—MICCAI 2024 Workshops, pp. 64–74, 2025.

• [21] N. Heller, F. Isensee, D. Trofimova, R. Tejpaul, Z. Zhao, H. Chen, L. Wang, A. Golts, D. Khapun, D. Shats, et al., "The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT," arXiv preprint arXiv:2307.01984, 2023.

• [22] S. Engelson, J. Ehrhardt, T. Kepp, J. Niemeijer, and H. Handels, "LNQ Challenge 2023: Learning mediastinal lymph node segmentation with a probabilistic lymph node atlas," Machine Learning for Biomedical Imaging, vol. 2, MICCAI 2023 LNQ Challenge special issue, pp. 817–833, 2024.

• [23] H. R. Roth, L. Lu, A. Seff, K. M. Cherry, J. Hoffman, S. Wang, J. Liu, E. Turkbey, and R. M. Summers, "A new 2.5D representation for lymph node detection in CT," data set, 2015.

• [24] K. Yan, X. Wang, L. Lu, and R. M. Summers, "DeepLesion: Automated mining of large-scale lesion annotations and universal lesion detection with deep learning," Journal of Medical Imaging, vol. 5, no. 3, p. 036501, 2018.

• [25] J. Dexl, K. Jeblick, A. Mittermeier, B. Schachtner, A. T. Stüber, J. Topalis, M. Rokuss, F. Isensee, K. H. Maier-Hein, H. Kalisch, et al., "The autoPET3 Challenge: Automated lesion segmentation in whole-body PET/CT—multitracer multicenter generalization," arXiv preprint arXiv:2605.05775, 2026.

• [26] T. Küstner, F. Peisen, S. Gatidis, A. Wagner, O. Megne, A. Othman, A. Sanner, T. Loßau, J. H. Moltz, T. Kohlbrandt, and A. Hering, "Longitudinal-CT," data set, 2025.

• [27] Z. Wu, Q. Zhao, M. Hu, Y. Li, H. Xue, Z. Jiang, A. Stefanidis, Q. Wang, I. Razzak, Z. Ge, et al., "MSWAL: 3D multi-class segmentation of whole abdominal lesions dataset," in Medical Image Computing and Computer Assisted Intervention—MICCAI 2025, pp. 378–388, 2025.