{"id":43071,"date":"2023-11-20T03:08:04","date_gmt":"2023-11-20T09:08:04","guid":{"rendered":"https:\/\/stage.cancerimagingarchive.net\/collection\/pancreas-ct\/"},"modified":"2024-07-26T13:02:43","modified_gmt":"2024-07-26T18:02:43","slug":"pancreas-ct","status":"publish","type":"tcia_collection","link":"https:\/\/stage.cancerimagingarchive.net\/collection\/pancreas-ct\/","title":{"rendered":"PANCREAS-CT"},"featured_media":0,"template":"","class_list":["post-43071","tcia_collection","type-tcia_collection","status-publish"],"cancer_types":["Healthy Controls (non-cancer)"],"citations":[43057,43059,9225],"collection_doi":"10.7937\/K9\/TCIA.2016.tNB1kqBU","collection_download_info":"","collection_downloads":[43061,43063],"versions":[43069],"additional_resources":"The NCI Cancer Research Data Commons (CRDC) provides access to additional data and a cloud-based data science infrastructure that connects data sets with analytics tools to allow users to share, integrate, analyze, and visualize cancer research data.\r\n<ul>\r\n \t<li><a href=\"https:\/\/portal.imaging.datacommons.cancer.gov\/explore\/filters\/?collection_id=pancreas_ct\">Imaging Data Commons (IDC)<\/a> (Imaging Data)<\/li>\r\n \t<li>IDC Zenodo community datasets:\r\n<ul>\r\n \t<li><a href=\"https:\/\/zenodo.org\/doi\/10.5281\/zenodo.12130275\">Pancreas-CT-SEG: DICOM-converted manual segmentations of pancreas for the Pancreas-CT collection<\/a><\/li>\r\n<\/ul>\r\n<\/li>\r\n<\/ul>","cancer_locations":["Pancreas"],"collection_page_accessibility":"Public","publications_related":"","version_change_log_archived":"<h3>Version 2 (Current): Updated 2020\/09\/10<\/h3><table><colgroup> <col \/> <col \/> <\/colgroup><tbody><tr><th colspan=\"1\">Data Type<\/th><th colspan=\"1\">Download all or Query\/Filter<\/th><\/tr><tr><td colspan=\"1\">Images (DICOM, 9.3 GB)<\/td><td colspan=\"1\"><div><p><a href=\"\/wp-content\/uploads\/Pancreas-CT-20200910.tcia\" download=\"Pancreas-CT-20200910.tcia\"><button><i><\/i> Download<\/button><\/a>\u00a0 <a href=\"https:\/\/www.cancerimagingarchive.net\/nbia-search\/?CollectionCriteria=Pancreas-CT\"><button><i><\/i> Search<\/button><\/a>\u00a0<\/p><p>(Download requires\u00a0the\u00a0<a href=\"https:\/\/wiki.cancerimagingarchive.net\/display\/NBIA\/Downloading+TCIA+Images\">NBIA Data Retriever<\/a>)<\/p><\/div><\/td><\/tr><tr><td colspan=\"1\">Manual Annotations<\/td><td colspan=\"1\"><div><p><a href=\"\/wp-content\/uploads\/TCIA_pancreas_labels-02-05-2017.zip\" download=\"TCIA_pancreas_labels-02-05-2017.zip\"><button><i><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><\/tbody><\/table><p>Note: Previously posted cases #25 and #70 were found to be from the same scan as case #2, just cropped slightly differently, and were removed from this version of the dataset.<\/p><h3>Version 1 : Updated 2015\/12\/29<\/h3><table><colgroup> <col \/> <col \/> <\/colgroup><tbody><tr><th colspan=\"1\">Data Type<\/th><th colspan=\"1\">Download all or Query\/Filter<\/th><\/tr><tr><td colspan=\"1\">Images (DICOM, 10.2 GB)<\/td><td colspan=\"1\"><div><p>not available, see version 2<\/p><\/div><\/td><\/tr><tr><td colspan=\"1\">Manual Annotations<\/td><td colspan=\"1\"><div><p><a href=\"\/wp-content\/uploads\/TCIA_pancreas_labels-02-05-2017.zip\" download=\"TCIA_pancreas_labels-02-05-2017.zip\"><button><i><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><\/tbody><\/table><p>&nbsp;<\/p>","collection_status":"Complete","publications_using":"TCIA maintains\u00a0<a href=\"https:\/\/www.cancerimagingarchive.net\/publications\/\">a list of publications<\/a>\u00a0that leverage TCIA data.\u00a0If you have a manuscript you'd like to add please<a href=\"http:\/\/www.cancerimagingarchive.net\/support\/\">\u00a0contact the TCIA Helpdesk<\/a>.\u00a0Below is a list of such publications using this Collection:\r\n<ul>\r\n \t<li>Gibson, E., Giganti, F., Hu, Y., Bonmati, E., Bandula, S., Gurusamy, K., . . . Barratt, D. C. (2017). Towards Image-Guided Pancreas and Biliary Endoscopy: Automatic Multi-organ Segmentation on Abdominal CT with Dense Dilated Networks. Paper presented at the International Conference on Medical Image Computing and Computer-Assisted Intervention.<\/li>\r\n \t<li>Greenspan, H., van Ginneken, B., &amp; Summers, R. M. (2016). Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique. IEEE Transactions on Medical Imaging, 35(5), 1153-1159. doi:10.1109\/TMI.2016.2553401<\/li>\r\n \t<li>Shi, H., Lu, L., Yin, M., Zhong, C., &amp; Yang, F. (2023). Joint few-shot registration and segmentation self-training of 3D medical images. Biomedical Signal Processing and Control, 80. doi:<a href=\"https:\/\/doi.org\/10.1016\/j.bspc.2022.104294\">https:\/\/doi.org\/10.1016\/j.bspc.2022.104294<\/a><\/li>\r\n<\/ul>","related_analysis_results":[46301],"species":["Human"],"version_number":"2","collection_title":"Pancreas-CT","date_updated":"2020-09-10","related_collection":false,"subjects":"82","analysis_results":"","collection_short_title":"Pancreas-CT","data_types":["CT"],"version_change_log":"Note: Previously posted cases #25 and #70 were found to be from the same scan as case #2, just cropped slightly differently, and were removed from this version of the dataset.","collection_browse_title":"Pancreas-CT","detailed_description":"<h3>Data Example<\/h3>\r\n<a href=\"\/wp-content\/uploads\/image2016-2-9-114819.png\" rel=\"prettyPhoto noopener\"><img class=\"cm-inline-img-css alignright wp-image-825 size-medium\" src=\"\/wp-content\/uploads\/image2016-2-9-114819.png\" \/><\/a>\r\n<h3><strong>Note<\/strong><\/h3>\r\nThe DICOM files were created from anonymized volumetric images (Analyze and NifTI) using this from ITK: <a href=\"http:\/\/www.itk.org\/Doxygen\/html\/Examples_2IO_2ImageReadDicomSeriesWrite_8cxx-example.html\"> <em>http:\/\/www.itk.org\/Doxygen\/html\/Examples_2IO_2ImageReadDicomSeriesWrite_8cxx-example.html<\/em> <\/a> <em>.<\/em>","supporting_data":["Image Analyses"],"collection_featured_image":false,"collection_summary":"<p>The National Institutes of Health Clinical Center performed 82 abdominal contrast enhanced 3D CT scans (~70 seconds after intravenous contrast injection in portal-venous) from 53 male and 27 female subjects.\u00a0 Seventeen of the subjects are healthy kidney donors scanned prior to nephrectomy.\u00a0 The remaining 65 patients were selected by a radiologist from patients who neither had major abdominal pathologies nor pancreatic cancer lesions.\u00a0 Subjects' ages range from 18 to 76 years with a mean age of 46.8 \u00b1 16.7. The CT scans have resolutions of 512x512 pixels with varying pixel sizes and slice thickness between 1.5 \u2212 2.5 mm, acquired on Philips and Siemens MDCT scanners (120 kVp tube voltage).<\/p><p>A medical student manually performed slice-by-slice segmentations of the pancreas as ground-truth and these were verified\/modified by an experienced radiologist.<\/p>","collection_acknowledgements":"","collection_funding":"","hide_from_browse_table":"0","program":["Community"],"_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/43071","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections"}],"about":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/types\/tcia_collection"}],"version-history":[{"count":1,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/43071\/revisions"}],"predecessor-version":[{"id":48277,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/43071\/revisions\/48277"}],"wp:attachment":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media?parent=43071"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}