orthanc:~/imaging-credits

Imaging credits

Every image in this archive is a real, openly licensed research study from the NCI Imaging Data Commons (v24), relabelled to a fictional patient: the patient, date, institution and trial fields in the DICOM headers are rewritten, the pixel data is unchanged. 178 series in total. No head or face imaging, by design. Please cite the sources below when reusing them.

Source collections

  • A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities
    4 series used (MR 4) · license CC BY 3.0

    Vallières, M., Freeman, C. R., Skamene, S. R., & El Naqa, I. (2015). A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities (Version 1) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/k9/tcia.2015.7go2gsks

  • Data From CT COLONOGRAPHY (ACRIN 6664)
    4 series used (CT 4) · license CC BY 3.0

    Smith, K., Clark, K., Bennett, W. C., Nolan, T., Kirby, J., Wolfsberger, M., Moulton, J., Vendt, B., & Freymann, J. (2015). Data From CT COLONOGRAPHY (ACRIN 6664) (Version 2) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/k9/tcia.2015.nwtesay1

  • Data from the National Lung Screening Trial (NLST)
    6 series used (CT 6) · license CC BY 4.0

    National Lung Screening Trial Research Team. (2013). Data from the National Lung Screening Trial (NLST) (Version 3) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.hmq8-j677

  • Multi-center breast DCE-MRI data and segmentations from patients in the I-SPY 1/ACRIN 6657 trials
    39 series used (MR 27, SEG 12) · license CC BY 3.0

    Newitt, D., & Hylton, N. (2016). Multi-center breast DCE-MRI data and segmentations from patients in the I-SPY 1/ACRIN 6657 trials (Version 2) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/k9/tcia.2016.hdhpgjlk

  • Multimodality annotated HCC cases with and without advanced imaging segmentation
    7 series used (CT 6, SEG 1) · license CC BY 4.0

    Moawad, A. W., Fuentes, D., Morshid, A., Khalaf, A. M., Elmohr, M. M., Abusaif, A., Hazle, J. D., Kaseb, A. O., Hassan, M., Mahvash, A., Szklaruk, J., Qayyom, A., & Elsayes, K. (2021). Multimodality annotated HCC cases with and without advanced imaging segmentation (Version 1) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.5fna-0924

  • Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-MRI-US-Biopsy)
    20 series used (MR 15, US 5) · license CC BY 4.0

    Natarajan, S., Priester, A., Margolis, D., Huang, J., & Marks, L. (2020). Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-MRI-US-Biopsy) (Version 2) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.2020.a61ioc1a

  • Stony Brook University COVID-19 Positive Cases
    34 series used (CR 34) · license CC BY 4.0

    Saltz, J., Saltz, M., Prasanna, P., Moffitt, R., Hajagos, J., Bremer, E., Balsamo, J., & Kurc, T. (2021). Stony Brook University COVID-19 Positive Cases (Version 1) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.bbag-2923

  • The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma Collection (TCGA-KIRC)
    12 series used (CT 3, MR 9) · license CC BY 3.0

    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

Annotations (segmentations and structured reports)

  • AI-derived annotations for the NLST and NSCLC-Radiomics computed tomography imaging collections
    12 series used (SEG 3, SR 9) · license CC BY 4.0
    Annotations on: nlst

    Krishnaswamy, D., Bontempi, D., Clunie, D., Aerts, H., & Fedorov, A. (2023). AI-derived annotations for the NLST and NSCLC-Radiomics computed tomography imaging collections [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7473970

  • DICOM converted annotations for the Prostate-MRI-US-Biopsy collection
    13 series used (SEG 13) · license CC BY 4.0
    Annotations on: prostate_mri_us_biopsy

    Ciausu, C., Clunie, D., & Fedorov, A. (2023). DICOM converted annotations for the Prostate-MRI-US-Biopsy collection [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10069910

  • DICOM SR of clinical data and measurement for breast cancer collections to TCIA
    4 series used (SR 4) · license CC BY 3.0
    Annotations on: ispy1

    Clunie, D. A., Hickman, H., Ver Hoef, W., Hastak, S., Wagner, U., & Helton, E. (2019). DICOM SR of clinical data and measurement for breast cancer collections to TCIA (Version 1) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.2019.wgllssg1

  • Image segmentations produced by BAMF under the AIMI Annotations initiative
    9 series used (SEG 9) · license CC BY 4.0
    Annotations on: hcc_tace_seg, nlst, prostate_mri_us_biopsy

    Van Oss, J., Murugesan, G. K., McCrumb, D., & Soni, R. (2024). Image segmentations produced by BAMF under the AIMI Annotations initiative (Version v2.0.2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8345959

  • NLST-Sybil: Expert annotations of tumor regions in the NLST CT images
    2 series used (SR 2) · license CC BY 4.0
    Annotations on: nlst

    Krishnaswamy, D., Clunie, D. A., & Fedorov, A. (2025). NLST-Sybil: Expert annotations of tumor regions in the NLST CT images [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15643334

  • NLSTSeg: Expert lesion segmentations and radiomics features for NLST CT images
    3 series used (SEG 1, SR 2) · license CC BY 4.0
    Annotations on: nlst

    Krishnaswamy, D., Clunie, D. A., & Fedorov, A. (2025). NLSTSeg: Expert lesion segmentations and radiomics features for NLST CT images [Data set]. Zenodo. https://doi.org/10.5281/zenodo.17362624

  • TotalSegmentator-CT-Segmentations: TotalSegmentator segmentations and radiomics features for NCI Imaging Data Commons CT images
    9 series used (SEG 3, SR 6) · license CC BY 4.0
    Annotations on: nlst

    Thiriveedhi, V. K., Krishnaswamy, D., Clunie, D., & Fedorov, A. (2024). TotalSegmentator-CT-Segmentations: TotalSegmentator segmentations and radiomics features for NCI Imaging Data Commons CT images [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8347011

Also cite

  • Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging, 26(6), 1045-1057. https://doi.org/10.1007/s10278-013-9622-7

  • Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W. L., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., … Kikinis, R. (2023). National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence. RadioGraphics, 43(12). https://doi.org/10.1148/rg.230180

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dicom.adrianbernhard.ch · fictional patients · imaging courtesy of NCI Imaging Data Commons