Hugging Face's logo

Datasets:
nielsRocholl
/
universal-lesion-segmentation

Tasks:
Image Segmentation
Modalities:
3D
Image
ArXiv:
Tags:
medical-imaging
ct-scan
lesion-segmentation
3d
image
License:
Dataset card Files
xet
Community
Dataset Viewer
The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code:   ConfigNamesError
Exception:    ReadTimeout
Message:      (ReadTimeoutError("HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: 93e0c2a5-fbab-4c29-a4c8-b31935740bf3)')
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                                   ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1029, in dataset_module_factory
                  raise e1 from None
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1004, in dataset_module_factory
                  ).get_module()
                    ^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 590, in get_module
                  standalone_yaml_path = cached_path(
                                         ^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 180, in cached_path
                  ).resolve_path(url_or_filename)
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/huggingface_hub/hf_file_system.py", line 198, in resolve_path
                  repo_and_revision_exist, err = self._repo_and_revision_exist(repo_type, repo_id, revision)
                                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/huggingface_hub/hf_file_system.py", line 125, in _repo_and_revision_exist
                  self._api.repo_info(
                File "/usr/local/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
                  return fn(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/huggingface_hub/hf_api.py", line 2816, in repo_info
                  return method(
                         ^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
                  return fn(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/huggingface_hub/hf_api.py", line 2673, in dataset_info
                  r = get_session().get(path, headers=headers, timeout=timeout, params=params)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/requests/sessions.py", line 602, in get
                  return self.request("GET", url, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/requests/sessions.py", line 589, in request
                  resp = self.send(prep, **send_kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/requests/sessions.py", line 703, in send
                  r = adapter.send(request, **kwargs)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 96, in send
                  return super().send(request, *args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/requests/adapters.py", line 690, in send
                  raise ReadTimeout(e, request=request)
              requests.exceptions.ReadTimeout: (ReadTimeoutError("HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: 93e0c2a5-fbab-4c29-a4c8-b31935740bf3)')

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

Universal Lesion Segmentation Datasets

A collection of public medical imaging datasets for lesion segmentation in CT scans. These are the datasets exactly as downloaded from their original sources.

Datasets

This repository contains the following datasets:

  • CECT - Liver (primary). Luo J, Wang X, Zhang Y, et al. Comprehensive multi-phase three-dimensional contrast-enhanced CT imaging dataset for primary liver cancer. Scientific Data. 2025;12(1):768. doi:10.1038/s41597-025-05125-2. Link
  • CLM - Liver (metastases). Simpson AL, Beckers RCJ, Kieneker LM, et al. Preoperative CT and survival data for patients undergoing resection of colorectal liver metastases. Scientific Data. 2024;11(1):172. doi:10.1038/s41597-024-03053-5. Link
  • kits21 - Kidney tumor segmentation. Heller N, Isensee F, Maier-Hein KH, et al. The KiTS21 challenge: automatic segmentation of kidneys, renal tumors, and renal cysts in contrast-enhanced CT. arXiv preprint arXiv:2307.01984. 2023. Link
  • lidc - Lung nodules. Armato SG III, McLennan G, Bidaut L, et al. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans. Medical Physics. 2011;38(2):915–931. doi:10.1118/1.3528204. Link
  • lits - Liver tumor segmentation. Bilic P, Christ PF, Vorontsov E, et al. The Liver Tumor Segmentation Benchmark (LiTS). Medical Image Analysis. 2023;84:102680. doi:10.1016/j.media.2022.102680. Link
  • LNDb - Lung nodules. Pedrosa J, Morais P, Ramos I, et al. LNDb: A lung nodule database on computed tomography. arXiv preprint arXiv:1911.08434. 2019. Link
  • Longitudinal-CT - Whole body FDG-PET/CT. Küstner, T., Peisen, F., Gatidis, S., Wagner, A., Megne, O., Othman, A., Sanner, A., Loßau, T., Moltz, J. H., Kohlbrandt, T., & Hering, A. Longitudinal-CT. University of Tübingen. 2025. doi:10.57754/FDAT.qwsry-7t837. Link
  • MSD - Whole body. Antonelli M, Reinke A, Bakas S, et al. The Medical Segmentation Decathlon. Nature Communications. 2022;13(1):4128. doi:10.1038/s41467-022-30695-9. Link
  • MSWAL - Abdominal lesions. Wu X, Chen Z, Li Y, et al. MSWAL: 3D multi-class segmentation of whole abdominal lesions dataset. In: Lecture Notes in Computer Science. Springer Nature Switzerland; 2026:378–388. Link
  • tcia_ct_lymph_nodes - Lymph nodes. Roth HR, Lu L, Farag A, et al. A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations. In: Golland P, et al., eds. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. Lecture Notes in Computer Science. Vol. 8673. Springer; 2014:520–527. doi:10.1007/978-3-319-10404-1_65. Link
  • WAW-TACE - Liver (primary). Bartnik K, Parzych J, Peregud-Pogorzelska M, et al. WAW-TACE: A hepatocellular carcinoma multiphase CT dataset with segmentations, radiomics features, and clinical data. Radiology: Artificial Intelligence. 2024;6(6):e240296. doi:10.1148/ryai.240296. Link
  • WORC - Multiple radiomics datasets (incl. GIST, CRLM). Starmans MPA, Janssen T, van der Voort SR, et al. The WORC database: MRI and CT scans, segmentations, and clinical labels for 930 patients from six radiomics studies. Scientific Data. 2021;8(1):41. Link

License

⚠️ IMPORTANT: Each dataset has its own license.

  • Check the LICENSE file in each dataset folder before using any data
  • Licenses vary (CC BY, CC BY-SA, CC BY-NC, CC BY-NC-SA, CC BY-NC-ND, and others)
  • Do not assume a single license applies to this entire collection
  • Always verify license compliance before use

Usage

Datasets are provided as-is in their original formats (primarily NIfTI .nii.gz, some NRRD). Each dataset folder contains the data files exactly as distributed by the original source.

For questions or issues, please refer to the original dataset sources or open an issue on this repository.

Downloads last month
106

Papers for nielsRocholl/universal-lesion-segmentation