VISCERAL Silver Corpus

Since the original host page (https://visceral.eu/benchmarks/anatomy3-open/) is no longer accessible, we have obtained renewed permission from the original authors and affiliated institutions to make the VISCERAL dataset available again as part of CADS, including its original images and annotations.

The original images are provided under controlled access for academic, non-commercial research. Segmentations and other secondary data products are openly available here, while access to the original images requires an application.

👉 Apply for access via this form

License

Academic research use only
Non-commercial use only
Internal use only (not transferable, no sublicensing, no redistribution of original images)
Access subject to the Data Access Agreement (DAA) confirmed in the application form

Citation

Paper BibTeX:

@inproceedings{krenn2015creating,
  title={Creating a large-scale silver corpus from multiple algorithmic segmentations},
  author={Krenn, Markus and Dorfer, Matthias and Jim{\'e}nez del Toro, Oscar Alfonso and M{\"u}ller, Henning and Menze, Bjoern and Weber, Marc-Andre and Hanbury, Allan and Langs, Georg},
  booktitle={International MICCAI Workshop on Medical Computer Vision},
  pages={103--115},
  year={2015},
  organization={Springer}
}

@article{xu2025cads,
  title={CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography},
  author={Xu, Murong and Amiranashvili, Tamaz and Navarro, Fernando and Fritsak, Maksym and Hamamci, Ibrahim Ethem and Shit, Suprosanna and Wittmann, Bastian and Er, Sezgin and Christ, Sebastian M. and de la Rosa, Ezequiel and Deseoe, Julian and Graf, Robert and Möller, Hendrik and Sekuboyina, Anjany and Peeken, Jan C. and Becker, Sven and Baldini, Giulia and Haubold, Johannes and Nensa, Felix and Hosch, René and Mirajkar, Nikhil and Khalid, Saad and Zachow, Stefan and Weber, Marc-André and Langs, Georg and Wasserthal, Jakob and Ozdemir, Mehmet Kemal and Fedorov, Andrey and Kikinis, Ron and Tanadini-Lang, Stephanie and Kirschke, Jan S. and Combs, Stephanie E. and Menze, Bjoern},
  journal={arXiv preprint arXiv:2507.22953},
  year={2025}
}

Dataset description

Homepage: https://visceral.eu/benchmarks/anatomy3-open/ (currently down)

Number of CT volumes: 127

Contrast: Unenhanced whole-body CT and contrast-enhanced abdomen and thorax CT (iodine-based contrast agent)

CT body coverage: Whole-body (head to knee) and contrast-enhanced regions (corpus mandibulae to pelvis)

Does the dataset include any ground truth annotations?: Yes

Original GT annotation targets: Liver, spleen, pancreas, gallbladder, urinary bladder, aorta, trachea, right lung, left lung, sternum, thyroid gland, first lumbar vertebrae, right kidney, left kidney, right adrenal gland, left adrenal gland, right psoas major, left psoas major, right rectus abdominis, left rectus abdominis

Number of annotated CT volumes: 127

Annotator: Label fusion of multiple algorithms (k-means clustering, rule-based segmentation, multi-boost learning SSM search, multi-atlas registration)

Acquisition centers: Medizinische Universität Wien (MUW), Universitätsklinikum Heidelberg (UKL-HD), Agència d‟Avalució, I Qualitat en Salut (GENCAT) in Catalonia Spain.

Pathology/Disease: Whole-body: bone marrow neoplasms (e.g., multiple myeloma). Contrast-enhanced: malignant lymphoma.

Original dataset download link: -

Original dataset format: nifti