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Datasets:
nielsRocholl
/
universal-lesion-segmentation

Tasks:
Image Segmentation
Modalities:
3D
Image
ArXiv:
Tags:
medical-imaging
ct-scan
lesion-segmentation
3d
image
License:
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    ImportError
Message:      To support decoding NIfTI files, please install 'nibabel'.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2543, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2061, in __iter__
                  batch = formatter.format_batch(pa_table)
                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 472, in format_batch
                  batch = self.python_features_decoder.decode_batch(batch)
                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 234, in decode_batch
                  return self.features.decode_batch(batch, token_per_repo_id=self.token_per_repo_id) if self.features else batch
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2161, in decode_batch
                  decode_nested_example(self[column_name], value, token_per_repo_id=token_per_repo_id)
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1419, in decode_nested_example
                  return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/nifti.py", line 172, in decode_example
                  raise ImportError("To support decoding NIfTI files, please install 'nibabel'.")
              ImportError: To support decoding NIfTI files, please install 'nibabel'.

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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
  • MCT-LTDiag - Liver tumors (multi-phase CT, differential diagnosis). Wu XA, Su H, Hua Y, et al. A multi-phase CT dataset for automated differential diagnosis of liver tumors. Scientific Data. 2025. Includes multi-phase NIfTI volumes, tumor masks, tumor-level imaging features, and patient-level clinical metadata. doi:10.7910/DVN/S3RW15. 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.

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