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0021_tcia_cptac_luad/README_0021_tcia_cptac_luad.md

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+# CPTAC-LUAD – Clinical Proteomic Tumor Analysis Consortium Lung Adenocarcinoma Collection
+
+## License
+**CC BY 3.0**  
+[Creative Commons Attribution 3.0 Unported License](https://creativecommons.org/licenses/by/3.0/)
+
+## Citation
+Dataset:
+
+```bibtex
+National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC). (2018). The Clinical Proteomic Tumor Analysis Consortium Lung Adenocarcinoma Collection (CPTAC-LUAD) (Version 13) [dataset]. The Cancer Imaging Archive. https://doi.org/10.7937/k9/tcia.2018.pat12tbs
+```
+
+
+## Dataset description
+This collection contains subjects from the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium Lung Adenocarcinoma (CPTAC-LUAD) cohort. CPTAC is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. Radiology and pathology images from CPTAC patients are being collected and made publicly available by The Cancer Imaging Archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.
+
+**Number of CT volumes**: 133
+
+**Contrast**: With and without contrast
+
+**CT body coverage**: Abdomen, chest
+
+**Does the dataset include any ground truth annotations?**: No
+
+**Original GT annotation targets**: -
+
+**Number of annotated CT volumes**: -
+
+**Annotator**: -
+
+**Acquisition centers**: -
+
+**Pathology/Disease**: lung adenocarcinoma
+
+**Original dataset download link**: https://www.cancerimagingarchive.net/collection/cptac-luad/
+
+**Original dataset format**: DICOM

0022_tcia_ct_images_covid19/README_0022_tcia_ct_images_covid19.md

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+# CT Images in COVID-19
+
+## License
+**CC BY 4.0**  
+[Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/)
+
+## Citation
+Paper BibTeX:
+
+```bibtex
+@article{harmon2020artificial,
+  title={Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets},
+  author={Harmon, Stephanie A and Sanford, Thomas H and Xu, Sheng and Turkbey, Evrim B and Roth, Holger and Xu, Ziyue and Yang, Dong and Myronenko, Andriy and Anderson, Victoria and Amalou, Amel and others},
+  journal={Nature communications},
+  volume={11},
+  number={1},
+  pages={4080},
+  year={2020},
+  publisher={Nature Publishing Group UK London}
+}
+```
+
+Dataset:
+
+```bibtex
+An, P., Xu, S., Harmon, S. A., Turkbey, E. B., Sanford, T. H., Amalou, A., Kassin, M., Varble, N., Blain, M., Anderson, V., Patella, F., Carrafiello, G., Turkbey, B. T., & Wood, B. J. (2020). CT Images in COVID-19 [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.2020.GQRY-NC81
+```
+
+## Dataset description
+This dataset contains unenhanced chest CT scans from COVID-19 patients confirmed by RT-PCR, collected across multiple international centers. It includes both initial point-of-care scans and serial follow-up CTs, provided in NIfTI format for research on COVID-19 pneumonia detection and analysis.
+
+**Number of CT volumes**: 121
+
+**Contrast**: None (soft tissue reconstruction)
+
+**CT body coverage**: Chest
+
+**Does the dataset include any ground truth annotations?**: Yes
+
+**Original GT annotation targets**: COVID-19 pneumonia features such as ground-glass opacities
+
+**Number of annotated CT volumes**: -
+
+**Annotator**: Human + AI
+
+**Acquisition centers**: The Xiangyang NO.1 People’s Hospital Affiliated Hospital of Hubei University of Medicine in Hubei Province, China, (2) 147 patients from the Self-Defense Forces Central Hospital, Tokyo, Japan, (3) 130 patients from San Paolo Hospital, Milan, Italy, and (4) 16 patients from Cà Granda Ospedale Maggiore Policlinico Milano, Milan, Italy
+
+**Pathology/Disease**: COVID-19 pneumonia, which involves pathologies like lung infiltrates and opacities characteristic of viral pneumonia.
+
+**Original dataset download link**: https://www.cancerimagingarchive.net/collection/ct-images-in-covid-19/
+
+**Original dataset format**: nifti

0023_tcia_nsclc_radiomics/README_0023_tcia_nsclc_radiomics.md

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+# NSCLC Radiogenomics
+
+## License
+**CC BY 3.0**  
+[Creative Commons Attribution 3.0 Unported License](https://creativecommons.org/licenses/by/3.0/)
+
+## Citation
+Paper BibTeX:
+
+```bibtex
+@article{bakr2018radiogenomic,
+  title={A radiogenomic dataset of non-small cell lung cancer},
+  author={Bakr, Shaimaa and Gevaert, Olivier and Echegaray, Sebastian and Ayers, Kelsey and Zhou, Mu and Shafiq, Majid and Zheng, Hong and Benson, Jalen Anthony and Zhang, Weiruo and Leung, Ann NC and others},
+  journal={Scientific data},
+  volume={5},
+  number={1},
+  pages={1--9},
+  year={2018},
+  publisher={Nature Publishing Group}
+}
+```
+
+Dataset:
+
+```bibtex
+Bakr, S., Gevaert, O., Echegaray, S., Ayers, K., Zhou, M., Shafiq, M., Zheng, H., Zhang, W., Leung, A., Kadoch, M., Shrager, J., Quon, A., Rubin, D., Plevritis, S., & Napel, S. (2017). Data for NSCLC Radiogenomics (Version 4) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2017.7hs46erv
+```
+
+
+## Dataset description
+This dataset contains imaging, genomic, and clinical data for 211 patients with non-small cell lung cancer. It includes CT and PET/CT scans, tumor annotations, segmentation maps, and matched genomic profiles to support research in radiogenomics and prognostic biomarker discovery.
+
+**Number of CT volumes**: 131
+
+**Contrast**: -
+
+**CT body coverage**: Chest
+
+**Does the dataset include any ground truth annotations?**: Yes
+
+**Original GT annotation targets**: Lung tumors
+
+**Number of annotated CT volumes**:
+
+**Annotator**: Human
+
+**Acquisition centers**: Stanford University Medical Center and Palo Alto Veterans Affairs Healthcare System
+
+**Pathology/Disease**: Non-small cell lung cancer, with detailed imaging of lung tumors
+
+**Original dataset download link**: https://www.cancerimagingarchive.net/collection/nsclc-radiogenomics/
+
+**Original dataset format**: DICOM
+

0024_pancreas_ct/README_0024_pancreas_ct.md

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+# Pancreas-CT
+
+## License
+**CC BY 3.0**  
+[Creative Commons Attribution 3.0 International License](https://creativecommons.org/licenses/by/3.0/)
+
+## Citation
+Paper BibTeX:
+
+```bibtex
+@inproceedings{roth2015deeporgan,
+  title={Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation},
+  author={Roth, Holger R and Lu, Le and Farag, Amal and Shin, Hoo-Chang and Liu, Jiamin and Turkbey, Evrim B and Summers, Ronald M},
+  booktitle={International conference on medical image computing and computer-assisted intervention},
+  pages={556--564},
+  year={2015},
+  organization={Springer}
+}
+```
+
+Dataset:
+```bibtex
+Roth, H., Farag, A., Turkbey, E. B., Lu, L., Liu, J., & Summers, R. M. (2016). Data From Pancreas-CT (Version 2) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU
+```
+
+## Dataset description
+This dataset includes 82 abdominal contrast-enhanced 3D CT scans from 80 subjects, acquired ~70 seconds after intravenous contrast injection in the portal-venous phase. Pancreas segmentations were performed manually slice-by-slice by a medical student and verified by an experienced radiologist. The scans were obtained on Philips and Siemens MDCT scanners.
+
+**Number of CT volumes**: 80
+
+**Contrast**: Contrast-enhanced CT (~70s after intravenous contrast injection in portal-venous phase)
+
+**CT body coverage**: Abdomen
+
+**Does the dataset include any ground truth annotations?**: Yes
+
+**Original GT annotation targets**: Pancreas
+
+**Number of annotated CT volumes**: -
+
+**Annotator**: Human
+
+**Acquisition centers**: National Institutes of Health Clinical Center in Bethesda, MD, USA.
+
+**Pathology/Disease**: Healthy controls
+
+**Original dataset download link**: https://www.cancerimagingarchive.net/collection/pancreas-ct/
+
+**Original dataset format**: DICOM
+

0025_pancreatic_ct_cbct_seg/README_0025_pancreatic_ct_cbct_seg.md

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+# Pancreatic CT-CBCT Segmentation
+
+## License
+**CC BY 4.0**  
+[Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/)
+
+## Citation
+Paper BibTeX:
+
+```bibtex
+@article{han2021deep,
+  title={Deep-learning-based image registration and automatic segmentation of organs-at-risk in cone-beam CT scans from high-dose radiation treatment of pancreatic cancer},
+  author={Han, Xu and Hong, Jun and Reyngold, Marsha and Crane, Christopher and Cuaron, John and Hajj, Carla and Mann, Justin and Zinovoy, Melissa and Greer, Hastings and Yorke, Ellen and others},
+  journal={Medical physics},
+  volume={48},
+  number={6},
+  pages={3084--3095},
+  year={2021},
+  publisher={Wiley Online Library}
+}
+```
+
+Dataset:
+```bibtex
+Hong, J., Reyngold, M., Crane, C., Cuaron, J., Hajj, C., Mann, J., Zinovoy, M., Yorke, E., LoCastro, E., Apte, A. P., & Mageras, G. (2021). Breath-hold CT and cone-beam CT images with expert manual organ-at-risk segmentations from radiation treatments of locally advanced pancreatic cancer [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.ESHQ-4D90
+```
+
+## Dataset description
+This dataset contains planning contrast-enhanced CT and cone-beam CT scans from 40 pancreatic cancer patients treated with high-dose radiation. All scans were acquired during deep-inspiration breath-hold, and organs-at-risk were manually segmented by radiation oncologists following delineation guidelines. It is designed for evaluating CT-to-CBCT deformable registration and auto-segmentation methods.
+
+**Number of CT volumes**: 93
+
+**Contrast**: Cone-beam CT (CBCT) with planning contrast-enhanced CT (iodinated contrast)
+
+**CT body coverage**: Chest, abdomen
+
+**Does the dataset include any ground truth annotations?**: Yes
+
+**Original GT annotation targets**: Small bowel, stomach, and first two segments of the duodenum
+
+**Number of annotated CT volumes**: -
+
+**Annotator**: Human
+
+**Acquisition centers**: Memorial Sloan Kettering Cancer Center, New York
+
+**Pathology/Disease**: Pancreatic cancer treated with high-dose radiation therapy
+
+**Original dataset download link**: https://www.cancerimagingarchive.net/collection/pancreatic-ct-cbct-seg/
+
+**Original dataset format**: DICOM
+

0026_rider_lung_ct/README_0026_rider_lung_ct.md

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+# RIDER Lung CT
+
+## License
+**CC BY 4.0**  
+[Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/)
+
+## Citation
+Paper BibTeX:
+
+```bibtex
+@article{zhao2009evaluating,
+  title={Evaluating variability in tumor measurements from same-day repeat CT scans of patients with non--small cell lung cancer},
+  author={Zhao, Binsheng and James, Leonard P and Moskowitz, Chaya S and Guo, Pingzhen and Ginsberg, Michelle S and Lefkowitz, Robert A and Qin, Yilin and Riely, Gregory J and Kris, Mark G and Schwartz, Lawrence H},
+  journal={Radiology},
+  volume={252},
+  number={1},
+  pages={263--272},
+  year={2009},
+  publisher={Radiological Society of North America, Inc.}
+}
+```
+
+Dataset:
+```bibtex
+Zhao, B., Schwartz, L. H., Kris, M. G., & Riely, G. J. (2015). Coffee-break lung CT collection with scan images reconstructed at multiple imaging parameters (Version 3) [Dataset]. The Cancer Imaging Archive. https://doi.org/10.7937/k9/tcia.2015.u1x8a5nr
+```
+
+
+## Dataset description
+This dataset contains same-day repeat CT scans of 32 non–small cell lung cancer patients, each reconstructed at six imaging settings varying in slice thickness and reconstruction kernel. Radiologist-annotated lesion contours are included as reference standards, enabling evaluation of quantitative imaging biomarkers across different reconstruction parameters.
+
+**Number of CT volumes**: 59
+
+**Contrast**: Unenhanced
+
+**CT body coverage**: Chest
+
+**Does the dataset include any ground truth annotations?**: Yes
+
+**Original GT annotation targets**: Lung tumors
+
+**Number of annotated CT volumes**: -
+
+**Annotator**: Human
+
+**Acquisition centers**: Memorial Sloan-Kettering Cancer Center, New York
+
+**Pathology/Disease**: Non–small cell lung cancer with measurable primary or metastatic lesions
+
+**Original dataset download link**: https://www.cancerimagingarchive.net/collection/rider-lung-ct/
+
+**Original dataset format**: DICOM
+

0027_tcia_tcga_kich/README_0027_tcia_tcga_kich.md

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+# TCGA-KICH (Kidney Chromophobe)
+
+## License
+**CC BY 3.0**  
+[Creative Commons Attribution 3.0 International License](https://creativecommons.org/licenses/by/3.0/)
+
+
+## Citation
+Dataset:
+
+```bibtex
+Linehan, M. W., Gautam, R., Sadow, C. A., & Levine, S. (2016). The Cancer Genome Atlas Kidney Chromophobe Collection (TCGA-KICH) (Version 3) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.YU3RBCZN
+```
+
+
+## Dataset description
+This dataset is part of The Cancer Genome Atlas initiative, linking kidney chromophobe cancer imaging data with corresponding clinical, genetic, and pathological information. The CT scans are heterogeneous, originating from multiple institutions and acquired under varying clinical protocols.
+
+**Number of CT volumes**: 17
+
+**Contrast**: -
+
+**CT body coverage**: Abdomen
+
+**Does the dataset include any ground truth annotations?**: No
+
+**Original GT annotation targets**: -
+
+**Number of annotated CT volumes**: -
+
+**Annotator**: -
+
+**Acquisition centers**: -
+
+**Pathology/Disease**: Kidney chromophobe cancer
+
+**Original dataset download link**: https://www.cancerimagingarchive.net/collection/tcga-kich/
+
+**Original dataset format**: DICOM
+

0028_tcia_tcga_kirc/README_0028_tcia_tcga_kirc.md

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+# TCGA-KIRC (Kidney Renal Clear Cell Carcinoma)
+
+## License
+**CC BY 3.0**  
+[Creative Commons Attribution 3.0 International License](https://creativecommons.org/licenses/by/3.0/)
+
+## Citation
+Dataset:
+
+```bibtex
+Akin, O., Elnajjar, P., Heller, M., Jarosz, R., Erickson, B. J., Kirk, S., Lee, Y., Linehan, M. W., Gautam, R., Vikram, R., Garcia, K. M., Roche, C., Bonaccio, E., & Filippini, J. (2016). The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma Collection (TCGA-KIRC) (Version 3) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.V6PBVTDR
+```
+
+
+## Dataset description
+This dataset is part of The Cancer Genome Atlas initiative, providing CT imaging data for kidney renal clear cell carcinoma patients, linked with clinical, genetic, and pathological information. Images are heterogeneous, collected from multiple institutions under varying clinical protocols.
+
+**Number of CT volumes**: 398
+
+**Contrast**: -
+
+**CT body coverage**: Abdomen
+
+**Does the dataset include any ground truth annotations?**: No
+
+**Original GT annotation targets**: -
+
+**Number of annotated CT volumes**: -
+
+**Annotator**: -
+
+**Acquisition centers**: -
+
+**Pathology/Disease**: Kidney renal clear cell carcinoma
+
+**Original dataset download link**: https://www.cancerimagingarchive.net/collection/tcga-kirc/
+
+**Original dataset format**: DICOM

0029_tcia_tcga_kirp/README_0029_tcia_tcga_kirp.md

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+# TCGA-KIRP (Kidney Renal Papillary Cell Carcinoma)
+
+## License
+**CC BY 3.0**  
+[Creative Commons Attribution 3.0 International License](https://creativecommons.org/licenses/by/3.0/)
+
+## Citation
+Dataset:
+
+```bibtex
+Linehan, M., Gautam, R., Kirk, S., Lee, Y., Roche, C., Bonaccio, E., Filippini, J., Rieger-Christ, K., Lemmerman, J., & Jarosz, R. (2016). The Cancer Genome Atlas Cervical Kidney Renal Papillary Cell Carcinoma Collection (TCGA-KIRP) (Version 4) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.ACWOGBEF
+```
+
+
+## Dataset description
+Part of The Cancer Genome Atlas initiative, this collection provides CT imaging data for kidney renal papillary cell carcinoma patients, linked with clinical, genetic, and pathological information. Images are heterogeneous, collected from multiple institutions and routine clinical workflows.
+
+**Number of CT volumes**: 19
+
+**Contrast**: -
+
+**CT body coverage**: Abdomen
+
+**Does the dataset include any ground truth annotations?**: No
+
+**Original GT annotation targets**: -
+
+**Number of annotated CT volumes**: -
+
+**Annotator**: -
+
+**Acquisition centers**: -
+
+**Pathology/Disease**: Kidney renal papillary cell carcinoma
+
+**Original dataset download link**: https://www.cancerimagingarchive.net/collection/tcga-kirp/
+
+**Original dataset format**: DICOM

0030_tcia_tcga_lihc/README_0030_tcia_tcga_lihc.md

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+# TCGA-LIHC (Liver Hepatocellular Carcinoma)
+
+## License
+**CC BY 3.0**  
+[Creative Commons Attribution 3.0 International License](https://creativecommons.org/licenses/by/3.0/)
+
+## Citation
+Dataset:
+
+```bibtex
+Erickson, B. J., Kirk, S., Lee, Y., Bathe, O., Kearns, M., Gerdes, C., Rieger-Christ, K., & Lemmerman, J. (2016). The Cancer Genome Atlas Liver Hepatocellular Carcinoma Collection (TCGA-LIHC) (Version 5) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.IMMQW8UQ
+```
+
+
+## Dataset description
+Part of The Cancer Genome Atlas initiative, this collection provides CT imaging data for liver hepatocellular carcinoma patients, linked with clinical, genetic, and pathological information. The images are heterogeneous, originating from multiple institutions and routine clinical workflows.
+
+**Number of CT volumes**: 242
+
+**Contrast**: -
+
+**CT body coverage**: Abdomen
+
+**Does the dataset include any ground truth annotations?**: No
+
+**Original GT annotation targets**: -
+
+**Number of annotated CT volumes**: -
+
+**Annotator**: -
+
+**Acquisition centers**: -
+
+**Pathology/Disease**: Liver hepatocellular carcinoma
+
+**Original dataset download link**: https://www.cancerimagingarchive.net/collection/tcga-lihc/
+
+**Original dataset format**: DICOM

0032_stoic2021/README_0032_stoic2021.md

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+# STOIC (Study of Thoracic CT in COVID-19)
+
+## License
+**CC BY-NC 4.0**  
+[Creative Commons Attribution-NonCommercial 4.0 International License](https://creativecommons.org/licenses/by-nc/4.0/)
+
+## Citation
+Paper BibTeX:
+
+```bibtex
+@article{revel2021study,
+  title={Study of thoracic CT in COVID-19: the STOIC project},
+  author={Revel, Marie-Pierre and Boussouar, Samia and de Margerie-Mellon, Constance and Saab, In{\`e}s and Lapotre, Thibaut and Mompoint, Dominique and Chassagnon, Guillaume and Milon, Audrey and Lederlin, Mathieu and Bennani, Souhail and others},
+  journal={Radiology},
+  volume={301},
+  number={1},
+  pages={E361--E370},
+  year={2021},
+  publisher={Radiological Society of North America}
+}
+```
+
+
+## Dataset description
+Collected during March–April 2020 in France, this dataset contains chest CT scans from 10,735 individuals suspected of COVID-19 infection. For each patient in the training set, binary labels indicate COVID-19 presence (RT-PCR confirmed) and severity (intubation or death within one month). The public subset used here contains 2,000 scans as provided for the STOIC2021 challenge.
+
+**Challenge homepage**: https://stoic2021.grand-challenge.org/
+
+**Number of CT volumes**: 2000
+
+**Contrast**: Mostly non-contrast; contrast-enhanced CT performed when pulmonary embolism was suspected
+
+**CT body coverage**: Chest
+
+**Does the dataset include any ground truth annotations?**: No (only labels for classification tasks)
+
+**Original GT annotation targets**: -
+
+**Number of annotated CT volumes**: -
+
+**Annotator**: -
+
+**Acquisition centers**: 20 university hospitals: 15 from Assistance Publique des Hôpitaux de Paris and five from other cities (Strasbourg, Lyon, Rennes, and Montpellier). 
+
+**Pathology/Disease**: COVID-19 pneumonia, suspected of being infected with SARS-COV-2 during the first wave of the pandemic
+
+**Original dataset download link**: https://registry.opendata.aws/stoic2021-training/
+
+**Original dataset format**: .mha
+
+## Note
+This version uses the publicly available 2,000-scan subset released for the STOIC2021 challenge.

0034_empire/README_0034_empire.md

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+# EMPIRE10 Challenge
+
+## License
+Customized license:
+
+The following rules apply to those who register a team and download the data:
+
+The downloaded data sets or any data derived from these data sets, may not be given or redistributed under any circumstances to persons not belonging to the registered team.
+
+Data downloaded from this site may only be used for the purpose of preparing an entry to be submitted on this site. The data may not be used for other purposes in scientific studies and may not be used to train or develop other algorithms, including but not limited to algorithms used in commercial products.
+
+If the results of algorithms in this challenge are to be used in scientific publications (journal publications, conference papers, technical reports, presentations at conferences and meetings) you must make an appropriate citation. Murphy et al., "Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge.", IEEE Trans Med Imaging. 2011 Nov;30(11):1901-20.
+
+Teams must notify the organisers of EMPIRE10 about any publication that is (partly) based on the results data published on this site, in order for us to maintain a list of publications associated with the challenge.
+
+
+## Citation
+Paper BibTeX:
+
+```bibtex
+@article{murphy2011evaluation,
+  title={Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge},
+  author={Murphy, Keelin and Van Ginneken, Bram and Reinhardt, Joseph M and Kabus, Sven and Ding, Kai and Deng, Xiang and Cao, Kunlin and Du, Kaifang and Christensen, Gary E and Garcia, Vincent and others},
+  journal={IEEE transactions on medical imaging},
+  volume={30},
+  number={11},
+  pages={1901--1920},
+  year={2011},
+  publisher={IEEE}
+}
+```
+
+
+## Dataset description
+
+**Challenge homepage**: https://empire10.grand-challenge.org/
+
+**Number of CT volumes**: 60
+
+**Contrast**: CT scans acquired at different breathing phases (e.g., full inspiration and expiration) and from 4D respiratory datasets
+
+**CT body coverage**: Chest
+
+**Does the dataset include any ground truth annotations?**: Yes
+
+**Original GT annotation targets**: Lungs
+
+**Number of annotated CT volumes**: 60
+
+**Annotator**: Human + automated algorithm
+
+**Acquisition centers**: Nelson Study (Dutch–Belgian lung-cancer screening trial) which is the largest lung cancer screening trial in Europe who lived in four selected regions in the Netherlands and Belgium
+
+**Pathology/Disease**: Subjects may present with lung disease or be healthy
+
+**Original dataset download link**: https://drive.google.com/drive/folders/1yHWLQEK9c1xzggkCC4VX0X4To7BBDqu5
+
+**Original dataset format**: .mhd/.raw
+
+## Note
+In accordance with the license, CADS does **not** redistribute EMPIRE10 images. Please download the original images directly from the provided GoogleDrive link above.

0037_totalsegmentator/README_0037_totalsegmentator.md

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+# TotalSegmentator
+
+## License
+**CC BY 4.0**  
+[Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/)
+
+## Citation
+Paper BibTeX:
+
+```bibtex
+@article{wasserthal2023totalsegmentator,
+  title={TotalSegmentator: robust segmentation of 104 anatomic structures in CT images},
+  author={Wasserthal, Jakob and Breit, Hanns-Christian and Meyer, Manfred T and Pradella, Maurice and Hinck, Daniel and Sauter, Alexander W and Heye, Tobias and Boll, Daniel T and Cyriac, Joshy and Yang, Shan and others},
+  journal={Radiology: Artificial Intelligence},
+  volume={5},
+  number={5},
+  pages={e230024},
+  year={2023},
+  publisher={Radiological Society of North America}
+}
+```
+
+
+## Dataset description
+The TotalSegmentator dataset comprises 1204 CT images with expert-refined annotations for 104 anatomical structures, including organs, bones, muscles, and vessels. Images were sampled from routine clinical practice, encompassing a variety of pathologies, scanner types, acquisition phases, and institutions, ensuring strong generalizability to real-world applications.
+
+**Number of CT volumes**: 1203
+
+**Contrast**: Multiple contrast phases (native, arterial, portal venous, late phase, others); includes dual-energy CT
+
+**CT body coverage**: Various
+
+**Does the dataset include any ground truth annotations?**: Yes
+
+**Original GT annotation targets**: 104 structures (27 organs, 59 bones, 10 muscles, 8 vessels)
+
+**Number of annotated CT volumes**: 1203
+
+**Annotator**: AI + human refinement
+
+**Acquisition centers**: University Hospital Basel
+
+**Pathology/Disease**: 404 normal patients; 645 with abnormalities (tumor, vascular, trauma, inflammation, bleeding, others)
+
+**Original dataset download link**: https://zenodo.org/records/6802614
+
+**Original dataset format**: nifti
+
+## Note
+This work uses TotalSegmentator dataset version 1.0. We began with v1 early in the project, and it became the basis for subsequent developments. Switching entirely to v2 would require substantial rework; although v2 contains additional images and structures, we consider our current use and planned label release of v1 valid. For model comparisons shown in our paper, we employ the latest TS model to ensure a fair and up-to-date evaluation of strengths and limitations.

0038_amos/README_0038_amos.md

@@ -0,0 +1,47 @@
+# AMOS (Multi-Modality Abdominal Multi-Organ Segmentation Challenge)
+
+## License
+**CC BY 4.0**  
+[Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/)
+
+## Citation
+Paper BibTeX:
+
+```bibtex
+@article{ji2022amos,
+  title={Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation},
+  author={Ji, Yuanfeng and Bai, Haotian and Ge, Chongjian and Yang, Jie and Zhu, Ye and Zhang, Ruimao and Li, Zhen and Zhanng, Lingyan and Ma, Wanling and Wan, Xiang and others},
+  journal={Advances in neural information processing systems},
+  volume={35},
+  pages={36722--36732},
+  year={2022}
+}
+```
+
+
+## Dataset description
+AMOS is a large-scale abdominal multi-organ segmentation benchmark designed to advance clinical applications such as disease diagnosis and treatment planning. It contains 500 CT and 100 MRI scans from multi-center, multi-vendor, multi-modality, and multi-phase acquisitions, covering patients with a variety of abdominal diseases. Each case includes voxel-level annotations for 15 abdominal organs, enabling the development and fair comparison of versatile segmentation algorithms.
+
+**Challenge homepage**: https://amos22.grand-challenge.org/
+
+**Number of CT volumes**: 200
+
+**Contrast**: Contrast and non-contrast
+
+**CT body coverage**: Abdomen
+
+**Does the dataset include any ground truth annotations?**: Yes
+
+**Original GT annotation targets**: (15 abdominal organs) spleen, right kidney, left kidney, gallbladder, esophagus, liver, stomach, aorta, inferior vena cava, pancreas, right adrenal gland, left adrenal gland, duodenum, bladder, prostate/uterus
+
+**Number of annotated CT volumes**: 200
+
+**Annotator**: AI + human refinement
+
+**Acquisition centers**: Longgang District Central Hospital (SZ, CHINA) and Longgang District People's Hospital (SZ, CHINA).
+
+**Pathology/Disease**: Patients diagnosed with abdominal tumors or other abnormalities; normal abdomen cases excluded
+
+**Original dataset download link**: https://zenodo.org/records/7262581
+
+**Original dataset format**: nifti

0039_han_seg/README_0039_han_seg.md

@@ -0,0 +1,52 @@
+# HaN-Seg: The head and neck organ-at-risk CT & MR segmentation dataset
+
+## License
+**CC BY-NC-ND 4.0**  
+[Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License](https://creativecommons.org/licenses/by-nc-nd/4.0/)
+
+## Citation
+Paper BibTeX:
+
+```bibtex
+@article{podobnik2023han,
+  title={HaN-Seg: The head and neck organ-at-risk CT and MR segmentation dataset},
+  author={Podobnik, Ga{\v{s}}per and Strojan, Primo{\v{z}} and Peterlin, Primo{\v{z}} and Ibragimov, Bulat and Vrtovec, Toma{\v{z}}},
+  journal={Medical physics},
+  volume={50},
+  number={3},
+  pages={1917--1927},
+  year={2023},
+  publisher={Wiley Online Library}
+}
+```
+
+
+## Dataset description
+HaN-Seg provides anonymized CT and T1-weighted MR scans of 42 head and neck cancer patients acquired for image-guided radiotherapy planning. Each CT scan includes expert-curated binary masks for 30 organs-at-risk (OARs), enabling research on multimodal image analysis and precise radiotherapy planning.
+
+**Challenge homepage**: https://han-seg2023.grand-challenge.org/
+
+**Number of CT volumes**: 42
+
+**Contrast**: -
+
+**CT body coverage**: Head and neck
+
+**Does the dataset include any ground truth annotations?**: Yes
+
+**Original GT annotation targets**: (30 OARs) arytenoids, brainstem, carotid artery, cervical esophagus, cochlea, cricopharyngeal inlet, lacrimal gland, larynx—glottis, larynx—supraglottic, lips, mandible, optic chiasm, optic nerve, oral cavity, parotid gland, pituitary gland, spinal cord, submandibular gland, and thyroid
+
+**Number of annotated CT volumes**:42
+
+**Annotator**: Human
+
+**Acquisition centers**: Institute of Oncology Ljubljana, Slovenia
+
+**Pathology/Disease**: -
+
+**Original dataset download link**: https://zenodo.org/records/7442914#.ZBtfBHbMJaQ
+
+**Original dataset format**: nrrd
+
+## Note
+In accordance with the license, CADS does not redistribute HaN-Seg images or any derivative works. We are currently working on obtaining permission from the dataset authors to share example images with our segmentations.

0040_saros/README_0040_saros.md

@@ -0,0 +1,55 @@
+# SAROS: A dataset for whole-body region and organ segmentation in CT imaging
+
+## License
+Mix of **[CC BY 3.0](https://creativecommons.org/licenses/by/3.0/)**, **[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)**, and **[CC BY-NC 3.0](https://creativecommons.org/licenses/by-nc/3.0/)**.
+
+## Citation
+Paper BibTeX:
+
+```bibtex
+@article{koitka2024saros,
+  title={SAROS: A dataset for whole-body region and organ segmentation in CT imaging},
+  author={Koitka, Sven and Baldini, Giulia and Kroll, Lennard and van Landeghem, Natalie and Pollok, Olivia B and Haubold, Johannes and Pelka, Obioma and Kim, Moon and Kleesiek, Jens and Nensa, Felix and others},
+  journal={Scientific Data},
+  volume={11},
+  number={1},
+  pages={483},
+  year={2024},
+  publisher={Nature Publishing Group UK London}
+}
+```
+
+Dataset:
+```bibtex
+Koitka, S., Baldini, G., Kroll, L., van Landeghem, N., Haubold, J., Sung Kim, M., Kleesiek, J., Nensa, F., & Hosch, R. (2023). SAROS – A large, heterogeneous, and sparsely annotated segmentation dataset on CT imaging data (SAROS) (Version 2) [Data set]. The Cancer Imaging Archive. https://doi.org/10.25737/SZ96-ZG60
+```
+
+
+## Dataset description
+Sparsely Annotated Region and Organ Segmentation (SAROS) is a large, heterogeneous CT segmentation dataset from TCIA, designed to support body composition analysis. Initial annotations were generated using in-house segmentation models and manually refined on every fifth axial slice, with remaining slices labeled as “ignore” (value 255).
+
+It includes 900 CT series from 882 patients, sampled from multiple TCIA collections such as ACRIN-FLT-Breast, ACRIN-HNSCC-FDG-PET/CT, ACRIN-NSCLC-FDG-PET, C4KC-KiTS, LIDC-IDRI, Pancreas-CT, QIN-HEADNECK, and various TCGA cohorts.
+
+**Number of CT volumes**: 900
+
+**Contrast**: -
+
+**CT body coverage**: Various
+
+**Does the dataset include any ground truth annotations?**: Yes
+
+**Original GT annotation targets**: Subcutaneous tissue, muscle, abdominal cavity, thoracic cavity, bones, gland structure, pericardium, prosthetic breast implant, mediastinum
+
+**Number of annotated CT volumes**: 900
+
+**Annotator**: In-house segmentation models + human refinement
+
+**Acquisition centers**: -
+
+**Pathology/Disease**: -
+
+**Original dataset download link**: https://www.cancerimagingarchive.net/analysis-result/saros/
+
+A script to download and resample the images in GitHub repository: https://github.com/UMEssen/saros-dataset
+
+**Original dataset format**: DICOM

0041_CTRATE/README_0041_CTRATE.md

@@ -0,0 +1,42 @@
+# CT-RATE
+
+## License
+**CC BY-NC-SA 4.0**  
+[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/)
+
+## Citation
+Paper BibTeX:
+
+```bibtex
+@article{hamamci2024developing,
+  title={Developing generalist foundation models from a multimodal dataset for 3d computed tomography},
+  author={Hamamci, Ibrahim Ethem and Er, Sezgin and Wang, Chenyu and Almas, Furkan and Simsek, Ayse Gulnihan and Esirgun, Sevval Nil and Doga, Irem and Durugol, Omer Faruk and Dai, Weicheng and Xu, Murong and others},
+  journal={arXiv preprint arXiv:2403.17834},
+  year={2024}
+}
+```
+
+## Dataset description
+CT-RATE is a large-scale multimodal dataset pairing non-contrast chest CT volumes with radiology text reports, multi-abnormality labels, and metadata. It contains 25,692 chest CT volumes (expanded to 50,188 through reconstructions) from 21,304 unique patients, enabling research on vision–language learning and abnormality detection in 3D medical imaging.
+
+**Number of CT volumes**: 3134
+
+**Contrast**: Non-contrast
+
+**CT body coverage**: Chest
+
+**Does the dataset include any ground truth annotations?**: No
+
+**Original GT annotation targets**: -
+
+**Number of annotated CT volumes**: -
+
+**Annotator**: -
+
+**Acquisition centers**: -
+
+**Pathology/Disease**: 18 distinct abnormalities
+
+**Original dataset download link**: https://huggingface.co/datasets/ibrahimhamamci/CT-RATE
+
+**Original dataset format**: nifti

0042_new_brainct_1mm/README_0042_new_brainct_1mm.md

@@ -0,0 +1,50 @@
+# CADS (Newly Released) - BrainCT-1mm
+
+## License
+**CC BY 4.0**  
+[Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/)
+
+## Citation
+Paper BibTeX:
+
+```bibtex
+@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 others},
+  journal={arXiv preprint arXiv:2507.22953},
+  year={2025}
+}
+```
+
+## Dataset description
+This dataset contains 484 anonymized head CT scans from patients aged ≥ 18 years, collected at Istanbul Medipol University, further contributing to the available cranial imaging resources.
+
+All scans were acquired on the Philips iCT 256 scanner at MEDIPOL Radiology, Istanbul, using an identical protocol (“BEYIN YENI/BEYIN”) with:
+- 1 mm slice thickness
+- Helical acquisition mode
+- UB kernel
+- Consistent exposure settings (1.279 ms, 188 mA, 240 mAs)
+
+Patient positioning and image orientation were uniform across all cases.
+
+**Number of CT volumes**: 484
+
+**Contrast**: Non-contrast
+
+**CT body coverage**: Head and neck
+
+**Does the dataset include any ground truth annotations?**: No
+
+**Original GT annotation targets**: -
+
+**Number of annotated CT volumes**: -
+
+**Annotator**: -
+
+**Acquisition centers**: Istanbul Medipol University, Istanbul, Turkey
+
+**Pathology/Disease**: -
+
+**Original dataset download link**: CADS-dataset HuggingFace repository
+
+**Original dataset format**: nifti

0043_new_ct_tri/README_0043_new_ct_tri.md

@@ -0,0 +1,72 @@
+# CADS (Newly Released) - CT-TRI (Triphasic Contrast-Enhanced Abdominal CTs)
+
+## License
+**CC BY-NC-SA 4.0**  
+[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/)  
+
+A few images containing parts of the face have been defaced. It is the responsibility of every user to ensure that the data is still considered anonymized.
+
+## Citation
+Paper BibTeX:
+
+```bibtex
+@article{ruhling2022automated,
+  title={Automated detection of the contrast phase in MDCT by an artificial neural network improves the accuracy of opportunistic bone mineral density measurements},
+  author={R{\"u}hling, Sebastian and Navarro, Fernando and Sekuboyina, Anjany and El Husseini, Malek and Baum, Thomas and Menze, Bjoern and Braren, Rickmer and Zimmer, Claus and Kirschke, Jan S},
+  journal={European Radiology},
+  volume={32},
+  number={3},
+  pages={1465--1474},
+  year={2022},
+  publisher={Springer}
+}
+```
+
+## Dataset description
+CT images were retrospectively selected from our digital picture archiving communication system (PACS) (Sectra AB). We included 206 consecutive patients with a routine abdominal triphasic MDCT scan (dedicated to investigating liver or kidney pathologies) acquired between September 2016 and November 2019.
+
+Exclusion criteria were:
+- Previous contrast application < 2 h prior to the triphasic CT (n = 6)
+- Contrast administration via the inferior vena cava (n = 2)
+- Insufficient coverage of the abdomen (n = 5)
+
+The final dataset consisted of 193 adults (48 women and 145 men), with a mean age of 62.4 ± 14.6 years. Most patients included were suspected or confirmed to have liver or kidney cancer, resulting in a higher male-to-female ratio.
+
+All CT scans were performed on the same MDCT scanner (IQon Spectral CT; Philips Medical Care) using a standardized protocol. The routine abdominal contrast-enhanced images were acquired in helical mode with:
+- Peak tube voltage of 120 kVp
+- Axial slice thickness of 0.9–1 mm
+- Adaptive tube load
+
+After the acquisition of pre-contrast images, all patients received standardized intravenous administration of contrast agent (Iomeron 400; Bracco) using a high-pressure injector (Fresenius Pilot C; Fresenius Kabi). Thirty-seven patients additionally received oral contrast (Barilux Scan; Sanochemia Diagnostics).
+
+Post-contrast scans were performed in both arterial (AR) and portal venous (PV) phases:
+- AR phase: Triggered after a threshold of 120 HU was reached in a region of interest (ROI) in the aorta
+- PV phase: Performed after a standard delay of 80 s
+
+For further analysis, spine reformations were reconstructed using a filtered back projection favoring sharpness over noise (bone kernel). The contrast phase was visually assessed by two radiologists (2 and 19 years of clinical experience) and served as the ground truth.
+
+The CT data were converted into the Neuroimaging Informatics Technology Initiative (NIfTI) format and resampled to a maximum of 1 mm isotropic spatial resolution.
+
+(Additional information on the dataset can be found in the paper citation above)
+
+**Number of CT volumes**: 586
+
+**Contrast**: Triphasic contrast-enhanced (non-enhanced, arterial, and portal venous phases)
+
+**CT body coverage**: Abdomen (very few images, 6 cases, included partial face coverage)
+
+**Does the dataset include any ground truth annotations?**: No
+
+**Original GT annotation targets**: -
+
+**Number of annotated CT volumes**: -
+
+**Annotator**: -
+
+**Acquisition centers**: TUM Klinikum Rechts der Isar, Munich, Germany
+
+**Pathology/Disease**: Higher prevalence of liver and kidney pathologies
+
+**Original dataset download link**: CADS-dataset HuggingFace repository
+
+**Original dataset format**: nifti