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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:
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.
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.
This repository hosts the CADS-dataset, providing both original CT images and corresponding segmentation masks in their native spacing formats.
For more information on the dataset (data collection, labeling procedures, and model derivatives etc.), please refer to the CADS paper preprint.
Useful Links
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:
| Directory Name | Dataset Name | License | Number of CT Volumes | Details |
|---|---|---|---|---|
| 0001_visceral_gc | VISCERAL Gold Corpus | TBD | 40 | readme |
| 0002_visceral_sc | VISCERAL Silver Corpus | TBD | 127 | readme |
| 0003_kits21 | The Kidney and Kidney Tumor Segmentation Challenge （KiTS21） | CC BY-NC-SA 4.0 | 300 | readme |
| 0004_lits | Liver Tumor Segmentation Benchmark (LiTS) | CC BY-NC-SA 4.0 | 201 | readme |
| 0005_bcv_abdomen | MICCAI Multi-Atlas Labeling Beyond the Cranial Vault (Abdomen) | CC BY 4.0 | 50 | readme |
| 0006_bcv_cervix | MICCAI Multi-Atlas Labeling Beyond the Cranial Vault (Cervix) | CC BY 4.0 | 50 | readme |
| 0007_chaos | CHAOS – Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge (CT Subset) | CC BY-NC-SA 4.0 | 40 | readme |
| 0008_ctorg | CT-ORG: Multiple Organ Segmentation in CT | CC BY 3.0 | 140 | readme |
| 0009_abdomenct1k | AbdomenCT-1K | CC BY 4.0 | 1062 | readme |
| 0010_verse | VerSe – Vertebrae Labelling and Segmentation Benchmark | CC BY-SA 4.0 | 374 | readme |
| 0011_exact | EXACT'09 – Extraction of Airways from CT | Customized license | 40 | readme |
| 0012_cad_pe | CAD-PE – Computer Aided Detection for Pulmonary Embolism Challenge | CC BY 4.0 | 40 | readme |
| 0013_ribfrac | RibFrac Challenge Dataset | CC BY-NC 4.0 | 660 | readme |
| 0014_learn2reg | Learn2Reg – Abdomen MR-CT (TCIA Subset) | CC BY 3.0 and TCIA Data Usage Policy | 16 | readme |
| 0015_lndb | LNDb – Lung Nodule Database | CC BY-NC-ND 4.0 | 294 | readme |
| 0016_lidc | LIDC-IDRI – Lung Image Database Consortium and Image Database Resource Initiative | CC BY 3.0 | 997 | readme |
| 0017_lola11 | LOLA11 (LObe and Lung Analysis 2011) | Customized license | 55 | readme |
| 0018_sliver07 | SLIVER07 (Segmentation of the Liver 2007) | Customized license | 30 | readme |
| 0019_tcia_ct_lymph_nodes | Lymph Node CT Dataset (NIH, TCIA) | CC BY 3.0 | 174 | readme |
| 0020_tcia_cptac_ccrcc | CPTAC-CCRCC – Clear Cell Renal Cell Carcinoma | CC BY 3.0 | 258 | readme |
| 0021_tcia_cptac_luad | CPTAC-LUAD – Clinical Proteomic Tumor Analysis Consortium Lung Adenocarcinoma Collection | CC BY 3.0 | 133 | readme |
| 0022_tcia_ct_images_covid19 | CT Images in COVID-19 | CC BY 4.0 | 121 | readme |
| 0023_tcia_nsclc_radiomics | NSCLC Radiogenomics | CC BY 3.0 | 131 | readme |
| 0024_pancreas_ct | Pancreas-CT | CC BY 3.0 | 80 | readme |
| 0025_pancreatic_ct_cbct_seg | Pancreatic CT-CBCT Segmentation | CC BY 4.0 | 93 | readme |
| 0026_rider_lung_ct | RIDER Lung CT | CC BY 4.0 | 59 | readme |
| 0027_tcia_tcga_kich | TCGA-KICH (Kidney Chromophobe) | CC BY 3.0 | 17 | readme |
| 0028_tcia_tcga_kirc | TCGA-KIRC (Kidney Renal Clear Cell Carcinoma) | CC BY 3.0 | 398 | readme |
| 0029_tcia_tcga_kirp | TCGA-KIRP (Kidney Renal Papillary Cell Carcinoma) | CC BY 3.0 | 19 | readme |
| 0030_tcia_tcga_lihc | TCGA-LIHC (Liver Hepatocellular Carcinoma) | CC BY 3.0 | 242 | readme |
| 0032_stoic2021 | STOIC (Study of Thoracic CT in COVID-19) | CC BY-NC 4.0 | 2000 | readme |
| 0033_tcia_nlst | National Lung Screening Trial (NLST) | CC BY 4.0 | 7172 | readme |
| 0034_empire | EMPIRE10 Challenge | Customized license | 60 | readme |
| 0037_totalsegmentator | TotalSegmentator | CC BY 4.0 | 1203 | readme |
| 0038_amos | AMOS (Multi-Modality Abdominal Multi-Organ Segmentation Challenge) | CC BY 4.0 | 200 | readme |
| 0039_han_seg | HaN-Seg: The head and neck organ-at-risk CT & MR segmentation dataset | CC BY-NC-ND 4.0 | 42 | readme |
| 0040_saros | SAROS: A dataset for whole-body region and organ segmentation in CT imaging | Mix of CC BY 3.0, CC BY 4.0, and CC BY-NC 3.0 | 900 | readme |
| 0041_ctrate | CT-RATE | CC BY-NC-SA 4.0 | 3134 | readme |
| 0042_new_brainct_1mm | (Newly Released) BrainCT-1mm | CC BY 4.0 | 484 | readme |
| 0043_new_ct_tri | (Newly Released) CT-TRI (Triphasic Contrast-Enhanced Abdominal CTs) | CC BY-NC-SA 4.0 | 586 | readme |
Citation
If you find this work useful, or use the CADS-dataset in your research, 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}
}
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