mrmrx/CADS-dataset

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.

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

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

Update (2025-10-04): Fixed missing images and corrected affine/intensity errors in datasets 0010_verse, 0041_ctrate, and 0043_new_ct_tri, see details for affected IDs.

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	Customized license	40	readme
0002_visceral_sc	VISCERAL Silver Corpus	Customized license	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 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}
}