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active | true |
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title | Data Access |
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| Data AccessData Type | Download all or Query/Filter |
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Images and Segmentations (DICOM, XX.X GB) | (Download requires the NBIA Data Retriever) | Clinical data with description (XLSX) | | Software/Source Code (External weblink to github) | Tcia button generator |
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ext | true |
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label | Search |
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url | https://github.com/fuentesdt/livermask |
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Click the Versions tab for more info about data releases. Please contact help@cancerimagingarchive.net with any questions regarding usage. |
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title | Detailed Description |
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| Detailed DescriptionImage Statistics |
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Modalities | CT, SEG | Number of Patients | 105 | Number of Studies | 214 | Number of Series | 677 | Number of Images | 51,968 | Images Size (GB) | 26.6 |
These SEG were originally created as NIfTI format files (Amira Software, ThermoFisher 2019) , and converted to DICOM. Github link for the NN code: https://github.com/fuentesdt/livermask Note - the mask on Patient ID HCC_001 (SEG file Series UID 1.2.276.0.7230010.3.1.3.8323329.719.1600928570.399942) has a slightly different dimension than the CT (Series UI 1.3.6.1.4.1.14519.5.2.1.1706.8374.302065206690360709343725942120) . This difference is is far from the interesting features and the masks, so clinical interpretation should be unaffected by this discrepancy. |
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title | Citations & Data Usage Policy |
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| Citations & Data Usage Policy Tcia license 4 noncommercial |
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| DOI goes here. Create using Datacite with information from Collection Approval form Moawad A, Fuentes D, Elsayes K. Multimodality annotated HCC cases with and without advanced imaging segmentation. |
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title | Publication Citation |
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| Morshid, A., Elsayes, K. M., Khalaf, A. M., Elmohr, M. M., Yu, J., Kaseb, A. O., Hassan, M., Mahvash, A., Wang, Z., Hazle, J. D., & Fuentes, D. (2019). A Machine Learning Model to Predict Hepatocellular Carcinoma Response to Transcatheter Arterial Chemoembolization. Radiology: Artificial Intelligence, 1(5), e180021. https://doi.org/10.1148/ryai.2019180021 |
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| Only if they ask for special acknowledgments like funding sources, grant numbers, etc in their proposal. |
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| 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 |
Other Publications Using This DataTCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk. |
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| Version X (Current): Updated yyyy/mm/ddData Type | Download all or Query/Filter |
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Images (DICOM, xx.x GB) | | Clinical Data (CSV) | Link | Software/Source Code (web) | |
<< One or two sentences about what you changed since last version. No note required for version 1. >> |
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