license: other
license_name: cadsdataset
license_link: https://github.com/murong-xu/CADS
task_categories:

• image-segmentation
  tags:
• medical
• ct
• segmentation
• image
• 3d
• whole-body
• anatomy
  size_categories:
• 10K<n<100K
  configs:
  • config_name: 0001_visceral_gc
    data_files:
    • split: all
      path: "0001_visceral_gc/0001_visceral_gc.csv"
  • config_name: 0002_visceral_sc
    data_files:
    • split: all
      path: "0002_visceral_sc/0002_visceral_sc.csv"
  • config_name: 0003_kits21
    data_files:
    • split: all
      path: "0003_kits21/0003_kits21.csv"
  • config_name: 0004_lits
    data_files:
    • split: all
      path: "0004_lits/0004_lits.csv"
  • config_name: 0005_bcv_abdomen
    data_files:
    • split: all
      path: "0005_bcv_abdomen/0005_bcv_abdomen.csv"
  • config_name: 0006_bcv_cervix
    data_files:
    • split: all
      path: "0006_bcv_cervix/0006_bcv_cervix.csv"
  • config_name: 0007_chaos
    data_files:
    • split: all
      path: "0007_chaos/0007_chaos.csv"
  • config_name: 0008_ctorg
    data_files:
    • split: all
      path: "0008_ctorg/0008_ctorg.csv"
  • config_name: 0009_abdomenct1k
    data_files:
    • split: all
      path: "0009_abdomenct1k/0009_abdomenct1k.csv"
  • config_name: 0010_verse
    data_files:
    • split: all
      path: "0010_verse/0010_verse.csv"
  • config_name: 0011_exact
    data_files:
    • split: all
      path: "0011_exact/0011_exact.csv"
  • config_name: 0012_cad_pe
    data_files:
    • split: all
      path: "0012_cad_pe/0012_cad_pe.csv"
  • config_name: 0013_ribfrac
    data_files:
    • split: all
      path: "0013_ribfrac/0013_ribfrac.csv"
  • config_name: 0014_learn2reg
    data_files:
    • split: all
      path: "0014_learn2reg/0014_learn2reg.csv"
  • config_name: 0015_lndb
    data_files:
    • split: all
      path: "0015_lndb/0015_lndb.csv"
  • config_name: 0016_lidc
    data_files:
    • split: all
      path: "0016_lidc/0016_lidc.csv"
  • config_name: 0017_lola11
    data_files:
    • split: all
      path: "0017_lola11/0017_lola11.csv"
  • config_name: 0018_sliver07
    data_files:
    • split: all
      path: "0018_sliver07/0018_sliver07.csv"
  • config_name: 0019_tcia_ct_lymph_nodes
    data_files:
    • split: all
      path: "0019_tcia_ct_lymph_nodes/0019_tcia_ct_lymph_nodes.csv"
  • config_name: 0020_tcia_cptac_ccrcc
    data_files:
    • split: all
      path: "0020_tcia_cptac_ccrcc/0020_tcia_cptac_ccrcc.csv"
  • config_name: 0021_tcia_cptac_luad
    data_files:
    • split: all
      path: "0021_tcia_cptac_luad/0021_tcia_cptac_luad.csv"
  • config_name: 0022_tcia_ct_images_covid19
    data_files:
    • split: all
      path: "0022_tcia_ct_images_covid19/0022_tcia_ct_images_covid19.csv"
  • config_name: 0023_tcia_nsclc_radiomics
    data_files:
    • split: all
      path: "0023_tcia_nsclc_radiomics/0023_tcia_nsclc_radiomics.csv"
  • config_name: 0024_pancreas_ct
    data_files:
    • split: all
      path: "0024_pancreas_ct/0024_pancreas_ct.csv"
  • config_name: 0025_pancreatic_ct_cbct_seg
    data_files:
    • split: all
      path: "0025_pancreatic_ct_cbct_seg/0025_pancreatic_ct_cbct_seg.csv"
  • config_name: 0026_rider_lung_ct
    data_files:
    • split: all
      path: "0026_rider_lung_ct/0026_rider_lung_ct.csv"
  • config_name: 0027_tcia_tcga_kich
    data_files:
    • split: all
      path: "0027_tcia_tcga_kich/0027_tcia_tcga_kich.csv"
  • config_name: 0028_tcia_tcga_kirc
    data_files:
    • split: all
      path: "0028_tcia_tcga_kirc/0028_tcia_tcga_kirc.csv"
  • config_name: 0029_tcia_tcga_kirp
    data_files:
    • split: all
      path: "0029_tcia_tcga_kirp/0029_tcia_tcga_kirp.csv"
  • config_name: 0030_tcia_tcga_lihc
    data_files:
    • split: all
      path: "0030_tcia_tcga_lihc/0030_tcia_tcga_lihc.csv"
  • config_name: 0032_stoic2021
    data_files:
    • split: all
      path: "0032_stoic2021/0032_stoic2021.csv"
  • config_name: 0033_tcia_nlst
    data_files:
    • split: all
      path: "0033_tcia_nlst/0033_tcia_nlst.csv"
  • config_name: 0034_empire
    data_files:
    • split: all
      path: "0034_empire/0034_empire.csv"
  • config_name: 0037_totalsegmentator
    data_files:
    • split: all
      path: "0037_totalsegmentator/0037_totalsegmentator.csv"
  • config_name: 0038_amos
    data_files:
    • split: all
      path: "0038_amos/0038_amos.csv"
  • config_name: 0039_han_seg
    data_files:
    • split: all
      path: "0039_han_seg/0039_han_seg.csv"
  • config_name: 0040_saros
    data_files:
    • split: all
      path: "0040_saros/0040_saros.csv"
  • config_name: 0041_ctrate
    data_files:
    • split: all
      path: "0041_ctrate/0041_ctrate.csv"
  • config_name: 0042_new_brainct_1mm
    data_files:
    • split: all
      path: "0042_new_brainct_1mm/0042_new_brainct_1mm.csv"
  • config_name: 0043_new_ct_tri
    data_files:
    • split: all
      path: "0043_new_ct_tri/0043_new_ct_tri.csv"

CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography

Overview

CADS is a robust, fully automated framework for segmenting 167 anatomical structures in Computed Tomography (CT), spanning from head to knee regions across diverse anatomical systems.

The framework consists of two main components:

1. CADS-dataset:

   • 22,022 CT volumes with complete annotations for 167 anatomical structures.
   • Most extensive whole-body CT dataset, exceeding current collections in both scale (18x more CT scans) and anatomical coverage (60% more distinct targets).
   • Data collected from publicly available datasets and private hospital data, spanning 100+ imaging centers across 16 countries.
   • Diverse coverage of clinical variability, protocols, and pathological conditions.
   • Built through an automated pipeline with pseudo-labeling and unsupervised quality control.

2. CADS-model:

   • An open-source model suite for automated whole-body segmentation.
   • Performance validated on both public challenges and real-world hospital cohorts.
   • Available as Python script run (this GitHub repo) for flexible command-line usage.
   • Also available as a user-friendly 3D Slicer plugin with UI interface, simple installation and one-click inference.

For more information on the dataset (data collection, labeling procedures, and model derivatives etc.), please refer to the CADS paper preprint.

Useful Links

• 📄 CADS Paper Preprint
• 🤗 CADS-dataset
• 📦 CADS-model Weights
• 🔧 CADS-model Codebase
• 🛠 CADS-model 3D Slicer Plugin

Format

All images and segmentations are provided in NIfTI format, organized by data source.

The directory structure is as follows:

root/
├── dataset_name/
│   ├── images/         # Original CT volumes
│   ├── segmentations/  # Segmentation masks (indexing see [model labelmap](https://github.com/murong-xu/CADS/blob/main/resources/info/labelmap.md))
│   └── README.md       # Dataset license, citation, and further details

Important Notice

• We are not the original owners of the CT images, except for the BrainCT-1mm and CT-TRI datasets newly released in this project.
• Users should review the corresponding README.md file in each dataset subdirectory before using the data and decide whether to include or exclude that dataset based on their intended use.

Dataset Sources Overview

The CADS-dataset comprises multiple publicly available and private-source datasets, each released under its own license.

The table below summarizes all included sources:

Citation

If you use any component of CADS (CADS-dataset, its curated segmentation masks, pretrained CADS-model, or the 3D Slicer extension), please cite:

@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}
}