Images (NIfTI, 12.71 GB)<\/p>
First dataset<\/p><\/td>
<\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr>Images (NIfTI, 2 GB)<\/p>Second dataset<\/p><\/td><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><\/tbody><\/table>Added second dataset, 29 patients\/121 CT images.<\/p>Version 1: Updated 2020\/08\/31<\/h3><\/colgroup>Data Type<\/th>Download all or Query\/Filter<\/th><\/tr>Images (NIfTI, 12.71 GB)<\/p><\/td><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><\/tbody><\/table>","collection_status":"Complete","publications_using":"TCIA maintains\u00a0a list of publications<\/a> which leverage our data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk<\/a>.","related_analysis_results":false,"species":["Human"],"version_number":"2","collection_title":"CT Images in COVID-19","date_updated":"2021-05-25","related_collection":false,"subjects":"661","analysis_results":"","collection_short_title":"CT Images in COVID-19","data_types":["CT"],"version_change_log":"Added second dataset, 29 patients\/121 CT images.","collection_browse_title":"CT Images in COVID-19","detailed_description":"","supporting_data":false,"collection_featured_image":{"ID":"6917","post_author":"29","post_date":"2023-09-13 09:27:19","post_date_gmt":"2023-09-13 14:27:19","post_content":"","post_title":"COVID-TCIA1","post_excerpt":"","post_status":"inherit","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"covid-tcia1","to_ping":"","pinged":"","post_modified":"2023-12-07 08:24:50","post_modified_gmt":"2023-12-07 14:24:50","post_content_filtered":"","post_parent":"42049","guid":"https:\/\/stage.cancerimagingarchive.net\/wp-content\/uploads\/COVID-TCIA1.jpg","menu_order":"0","post_type":"attachment","post_mime_type":"image\/jpeg","comment_count":"0","pod_item_id":"6917"},"collection_summary":"These retrospective NIfTI image datasets consists of unenhanced chest CTs:\u00a0<\/p>First dataset - from 632 patients\u00a0with COVID-19 infections at initial point of care, and<\/li>Second dataset - a second set of 121\u00a0CTs from 29 patients\u00a0with COVID-19 infections with serial \/ sequential CTs.<\/li><\/ul>The initial images for both datasets were acquired\u00a0at the point of care in an outbreak setting\u00a0from patients with Reverse Transcription Polymerase Chain Reaction (RT-PCR) confirmation for the presence of SARS-CoV-2.<\/p><\/a>Patients presented to a health care setting with a combination of symptoms, exposure to an infected patient, or travel history to an outbreak region. All patients had a positive RT-PCR for SARS-CoV-2 from a sample obtained within 1 day of the initial CT. CT exams were performed without intravenous contrast and with a soft tissue reconstruction algorithm.\u00a0 The DICOM images were subsequently converted into NIfTI format.\u00a0The second dataset also had other follow up CTs, in addition to the initial point of care CT.<\/p>A multidisciplinary team trained several models using portions of the first dataset, along with additional CTs and manually annotated images from other sources. A classification model derived in part from the first dataset is described in a Nature Communications manuscript at:\u00a0\u00a0https:\/\/doi.org\/10.1038\/s41467-020-17971-2<\/a>.\u00a0 The\u00a0NVIDIA-related frameworks and models specific to this publication are available at no cost as part of the NVIDIA Clara Train SDK at\u00a0https:\/\/ngc.nvidia.com\/catalog\/containers\/nvidia:clara:ai-covid-19<\/a>.\u00a0This includes both inference-based pipelines for evaluation, as well as model weights for further training or fine tuning in outside institutions.\u00a0The second data set of\u00a0121 serial \/ sequential CTs in 29 patients is reported in a Scientific Reports manuscript at\u00a0\u00a0https:\/\/doi.org\/10.1038\/s41598-021-85694-5<\/a>.\u00a0<\/p>","collection_acknowledgements":"The Imaging AI in COVID team would like to acknowledge the following individuals who supported this multi-disciplinary multi-national team effort:<\/p>All frontline workers and Peng An, Sheng Xu, Evrim B. Turkbey, Stephanie A. Harmon, Thomas H. Sanford, Amel Amalou, Michael Kassin, Nicole Varble, Maxime Blain, Dilara Long, Dima Hammoud, Ashkan Malayeri, Elizabeth Jones, Holger Roth, Ziyue Xu, Dong Yang, Andriy Myronenko, Victoria Anderson, Mona Flores, Francesca Patella, Maurizio Cariati, Kaku Tamura, Hirofumi Obinata, Hitoshi Mori, Ulas Bagci, Daguang Xu, Hayet Amalou, Robert Suh, Gianpaolo Carrafiello, Baris Turkbey, Bradford J. Wood.<\/li>Thanks for leadership support to:\u00a0 John Gallin, Steve Holland, Cliff Lane, Bruce Tromberg, Tom Misteli, Bill Dahut.<\/li>Supported by the NIH Center for Interventional Oncology and the NIH Intramural Targeted Anti-COVID-19 (ITAC) Program.<\/li><\/ul>TCIA COVID-19 Datasets<\/h3>Additional datasets and information about TCIA efforts to support COVID-19 research can be found\u00a0here<\/a>.<\/p>","collection_funding":"","hide_from_browse_table":"0","program":["COVID-19"],"_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/42049","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\/42049\/revisions"}],"predecessor-version":[{"id":47527,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/42049\/revisions\/47527"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media\/6917"}],"wp:attachment":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media?parent=42049"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Images (NIfTI, 2 GB)<\/p>
Second dataset<\/p><\/td>
<\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><\/tbody><\/table>Added second dataset, 29 patients\/121 CT images.<\/p>Version 1: Updated 2020\/08\/31<\/h3><\/colgroup>Data Type<\/th>Download all or Query\/Filter<\/th><\/tr>Images (NIfTI, 12.71 GB)<\/p><\/td><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><\/tbody><\/table>","collection_status":"Complete","publications_using":"TCIA maintains\u00a0a list of publications<\/a> which leverage our data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk<\/a>.","related_analysis_results":false,"species":["Human"],"version_number":"2","collection_title":"CT Images in COVID-19","date_updated":"2021-05-25","related_collection":false,"subjects":"661","analysis_results":"","collection_short_title":"CT Images in COVID-19","data_types":["CT"],"version_change_log":"Added second dataset, 29 patients\/121 CT images.","collection_browse_title":"CT Images in COVID-19","detailed_description":"","supporting_data":false,"collection_featured_image":{"ID":"6917","post_author":"29","post_date":"2023-09-13 09:27:19","post_date_gmt":"2023-09-13 14:27:19","post_content":"","post_title":"COVID-TCIA1","post_excerpt":"","post_status":"inherit","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"covid-tcia1","to_ping":"","pinged":"","post_modified":"2023-12-07 08:24:50","post_modified_gmt":"2023-12-07 14:24:50","post_content_filtered":"","post_parent":"42049","guid":"https:\/\/stage.cancerimagingarchive.net\/wp-content\/uploads\/COVID-TCIA1.jpg","menu_order":"0","post_type":"attachment","post_mime_type":"image\/jpeg","comment_count":"0","pod_item_id":"6917"},"collection_summary":"These retrospective NIfTI image datasets consists of unenhanced chest CTs:\u00a0<\/p>First dataset - from 632 patients\u00a0with COVID-19 infections at initial point of care, and<\/li>Second dataset - a second set of 121\u00a0CTs from 29 patients\u00a0with COVID-19 infections with serial \/ sequential CTs.<\/li><\/ul>The initial images for both datasets were acquired\u00a0at the point of care in an outbreak setting\u00a0from patients with Reverse Transcription Polymerase Chain Reaction (RT-PCR) confirmation for the presence of SARS-CoV-2.<\/p><\/a>Patients presented to a health care setting with a combination of symptoms, exposure to an infected patient, or travel history to an outbreak region. All patients had a positive RT-PCR for SARS-CoV-2 from a sample obtained within 1 day of the initial CT. CT exams were performed without intravenous contrast and with a soft tissue reconstruction algorithm.\u00a0 The DICOM images were subsequently converted into NIfTI format.\u00a0The second dataset also had other follow up CTs, in addition to the initial point of care CT.<\/p>A multidisciplinary team trained several models using portions of the first dataset, along with additional CTs and manually annotated images from other sources. A classification model derived in part from the first dataset is described in a Nature Communications manuscript at:\u00a0\u00a0https:\/\/doi.org\/10.1038\/s41467-020-17971-2<\/a>.\u00a0 The\u00a0NVIDIA-related frameworks and models specific to this publication are available at no cost as part of the NVIDIA Clara Train SDK at\u00a0https:\/\/ngc.nvidia.com\/catalog\/containers\/nvidia:clara:ai-covid-19<\/a>.\u00a0This includes both inference-based pipelines for evaluation, as well as model weights for further training or fine tuning in outside institutions.\u00a0The second data set of\u00a0121 serial \/ sequential CTs in 29 patients is reported in a Scientific Reports manuscript at\u00a0\u00a0https:\/\/doi.org\/10.1038\/s41598-021-85694-5<\/a>.\u00a0<\/p>","collection_acknowledgements":"The Imaging AI in COVID team would like to acknowledge the following individuals who supported this multi-disciplinary multi-national team effort:<\/p>All frontline workers and Peng An, Sheng Xu, Evrim B. Turkbey, Stephanie A. Harmon, Thomas H. Sanford, Amel Amalou, Michael Kassin, Nicole Varble, Maxime Blain, Dilara Long, Dima Hammoud, Ashkan Malayeri, Elizabeth Jones, Holger Roth, Ziyue Xu, Dong Yang, Andriy Myronenko, Victoria Anderson, Mona Flores, Francesca Patella, Maurizio Cariati, Kaku Tamura, Hirofumi Obinata, Hitoshi Mori, Ulas Bagci, Daguang Xu, Hayet Amalou, Robert Suh, Gianpaolo Carrafiello, Baris Turkbey, Bradford J. Wood.<\/li>Thanks for leadership support to:\u00a0 John Gallin, Steve Holland, Cliff Lane, Bruce Tromberg, Tom Misteli, Bill Dahut.<\/li>Supported by the NIH Center for Interventional Oncology and the NIH Intramural Targeted Anti-COVID-19 (ITAC) Program.<\/li><\/ul>TCIA COVID-19 Datasets<\/h3>Additional datasets and information about TCIA efforts to support COVID-19 research can be found\u00a0here<\/a>.<\/p>","collection_funding":"","hide_from_browse_table":"0","program":["COVID-19"],"_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/42049","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\/42049\/revisions"}],"predecessor-version":[{"id":47527,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/42049\/revisions\/47527"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media\/6917"}],"wp:attachment":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media?parent=42049"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Added second dataset, 29 patients\/121 CT images.<\/p>
Images (NIfTI, 12.71 GB)<\/p><\/td>
<\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><\/tbody><\/table>","collection_status":"Complete","publications_using":"TCIA maintains\u00a0a list of publications<\/a> which leverage our data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk<\/a>.","related_analysis_results":false,"species":["Human"],"version_number":"2","collection_title":"CT Images in COVID-19","date_updated":"2021-05-25","related_collection":false,"subjects":"661","analysis_results":"","collection_short_title":"CT Images in COVID-19","data_types":["CT"],"version_change_log":"Added second dataset, 29 patients\/121 CT images.","collection_browse_title":"CT Images in COVID-19","detailed_description":"","supporting_data":false,"collection_featured_image":{"ID":"6917","post_author":"29","post_date":"2023-09-13 09:27:19","post_date_gmt":"2023-09-13 14:27:19","post_content":"","post_title":"COVID-TCIA1","post_excerpt":"","post_status":"inherit","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"covid-tcia1","to_ping":"","pinged":"","post_modified":"2023-12-07 08:24:50","post_modified_gmt":"2023-12-07 14:24:50","post_content_filtered":"","post_parent":"42049","guid":"https:\/\/stage.cancerimagingarchive.net\/wp-content\/uploads\/COVID-TCIA1.jpg","menu_order":"0","post_type":"attachment","post_mime_type":"image\/jpeg","comment_count":"0","pod_item_id":"6917"},"collection_summary":"These retrospective NIfTI image datasets consists of unenhanced chest CTs:\u00a0<\/p>First dataset - from 632 patients\u00a0with COVID-19 infections at initial point of care, and<\/li>Second dataset - a second set of 121\u00a0CTs from 29 patients\u00a0with COVID-19 infections with serial \/ sequential CTs.<\/li><\/ul>The initial images for both datasets were acquired\u00a0at the point of care in an outbreak setting\u00a0from patients with Reverse Transcription Polymerase Chain Reaction (RT-PCR) confirmation for the presence of SARS-CoV-2.<\/p><\/a>Patients presented to a health care setting with a combination of symptoms, exposure to an infected patient, or travel history to an outbreak region. All patients had a positive RT-PCR for SARS-CoV-2 from a sample obtained within 1 day of the initial CT. CT exams were performed without intravenous contrast and with a soft tissue reconstruction algorithm.\u00a0 The DICOM images were subsequently converted into NIfTI format.\u00a0The second dataset also had other follow up CTs, in addition to the initial point of care CT.<\/p>A multidisciplinary team trained several models using portions of the first dataset, along with additional CTs and manually annotated images from other sources. A classification model derived in part from the first dataset is described in a Nature Communications manuscript at:\u00a0\u00a0https:\/\/doi.org\/10.1038\/s41467-020-17971-2<\/a>.\u00a0 The\u00a0NVIDIA-related frameworks and models specific to this publication are available at no cost as part of the NVIDIA Clara Train SDK at\u00a0https:\/\/ngc.nvidia.com\/catalog\/containers\/nvidia:clara:ai-covid-19<\/a>.\u00a0This includes both inference-based pipelines for evaluation, as well as model weights for further training or fine tuning in outside institutions.\u00a0The second data set of\u00a0121 serial \/ sequential CTs in 29 patients is reported in a Scientific Reports manuscript at\u00a0\u00a0https:\/\/doi.org\/10.1038\/s41598-021-85694-5<\/a>.\u00a0<\/p>","collection_acknowledgements":"The Imaging AI in COVID team would like to acknowledge the following individuals who supported this multi-disciplinary multi-national team effort:<\/p>All frontline workers and Peng An, Sheng Xu, Evrim B. Turkbey, Stephanie A. Harmon, Thomas H. Sanford, Amel Amalou, Michael Kassin, Nicole Varble, Maxime Blain, Dilara Long, Dima Hammoud, Ashkan Malayeri, Elizabeth Jones, Holger Roth, Ziyue Xu, Dong Yang, Andriy Myronenko, Victoria Anderson, Mona Flores, Francesca Patella, Maurizio Cariati, Kaku Tamura, Hirofumi Obinata, Hitoshi Mori, Ulas Bagci, Daguang Xu, Hayet Amalou, Robert Suh, Gianpaolo Carrafiello, Baris Turkbey, Bradford J. Wood.<\/li>Thanks for leadership support to:\u00a0 John Gallin, Steve Holland, Cliff Lane, Bruce Tromberg, Tom Misteli, Bill Dahut.<\/li>Supported by the NIH Center for Interventional Oncology and the NIH Intramural Targeted Anti-COVID-19 (ITAC) Program.<\/li><\/ul>TCIA COVID-19 Datasets<\/h3>Additional datasets and information about TCIA efforts to support COVID-19 research can be found\u00a0here<\/a>.<\/p>","collection_funding":"","hide_from_browse_table":"0","program":["COVID-19"],"_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/42049","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\/42049\/revisions"}],"predecessor-version":[{"id":47527,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/42049\/revisions\/47527"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media\/6917"}],"wp:attachment":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media?parent=42049"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
These retrospective NIfTI image datasets consists of unenhanced chest CTs:\u00a0<\/p>
The initial images for both datasets were acquired\u00a0at the point of care in an outbreak setting\u00a0from patients with Reverse Transcription Polymerase Chain Reaction (RT-PCR) confirmation for the presence of SARS-CoV-2.<\/p>
<\/a>Patients presented to a health care setting with a combination of symptoms, exposure to an infected patient, or travel history to an outbreak region. All patients had a positive RT-PCR for SARS-CoV-2 from a sample obtained within 1 day of the initial CT. CT exams were performed without intravenous contrast and with a soft tissue reconstruction algorithm.\u00a0 The DICOM images were subsequently converted into NIfTI format.\u00a0The second dataset also had other follow up CTs, in addition to the initial point of care CT.<\/p>A multidisciplinary team trained several models using portions of the first dataset, along with additional CTs and manually annotated images from other sources. A classification model derived in part from the first dataset is described in a Nature Communications manuscript at:\u00a0\u00a0https:\/\/doi.org\/10.1038\/s41467-020-17971-2<\/a>.\u00a0 The\u00a0NVIDIA-related frameworks and models specific to this publication are available at no cost as part of the NVIDIA Clara Train SDK at\u00a0https:\/\/ngc.nvidia.com\/catalog\/containers\/nvidia:clara:ai-covid-19<\/a>.\u00a0This includes both inference-based pipelines for evaluation, as well as model weights for further training or fine tuning in outside institutions.\u00a0The second data set of\u00a0121 serial \/ sequential CTs in 29 patients is reported in a Scientific Reports manuscript at\u00a0\u00a0https:\/\/doi.org\/10.1038\/s41598-021-85694-5<\/a>.\u00a0<\/p>","collection_acknowledgements":"The Imaging AI in COVID team would like to acknowledge the following individuals who supported this multi-disciplinary multi-national team effort:<\/p>All frontline workers and Peng An, Sheng Xu, Evrim B. Turkbey, Stephanie A. Harmon, Thomas H. Sanford, Amel Amalou, Michael Kassin, Nicole Varble, Maxime Blain, Dilara Long, Dima Hammoud, Ashkan Malayeri, Elizabeth Jones, Holger Roth, Ziyue Xu, Dong Yang, Andriy Myronenko, Victoria Anderson, Mona Flores, Francesca Patella, Maurizio Cariati, Kaku Tamura, Hirofumi Obinata, Hitoshi Mori, Ulas Bagci, Daguang Xu, Hayet Amalou, Robert Suh, Gianpaolo Carrafiello, Baris Turkbey, Bradford J. Wood.<\/li>Thanks for leadership support to:\u00a0 John Gallin, Steve Holland, Cliff Lane, Bruce Tromberg, Tom Misteli, Bill Dahut.<\/li>Supported by the NIH Center for Interventional Oncology and the NIH Intramural Targeted Anti-COVID-19 (ITAC) Program.<\/li><\/ul>TCIA COVID-19 Datasets<\/h3>Additional datasets and information about TCIA efforts to support COVID-19 research can be found\u00a0here<\/a>.<\/p>","collection_funding":"","hide_from_browse_table":"0","program":["COVID-19"],"_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/42049","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\/42049\/revisions"}],"predecessor-version":[{"id":47527,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/42049\/revisions\/47527"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media\/6917"}],"wp:attachment":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media?parent=42049"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
A multidisciplinary team trained several models using portions of the first dataset, along with additional CTs and manually annotated images from other sources. A classification model derived in part from the first dataset is described in a Nature Communications manuscript at:\u00a0\u00a0https:\/\/doi.org\/10.1038\/s41467-020-17971-2<\/a>.\u00a0 The\u00a0NVIDIA-related frameworks and models specific to this publication are available at no cost as part of the NVIDIA Clara Train SDK at\u00a0https:\/\/ngc.nvidia.com\/catalog\/containers\/nvidia:clara:ai-covid-19<\/a>.\u00a0This includes both inference-based pipelines for evaluation, as well as model weights for further training or fine tuning in outside institutions.\u00a0The second data set of\u00a0121 serial \/ sequential CTs in 29 patients is reported in a Scientific Reports manuscript at\u00a0\u00a0https:\/\/doi.org\/10.1038\/s41598-021-85694-5<\/a>.\u00a0<\/p>","collection_acknowledgements":"The Imaging AI in COVID team would like to acknowledge the following individuals who supported this multi-disciplinary multi-national team effort:<\/p>All frontline workers and Peng An, Sheng Xu, Evrim B. Turkbey, Stephanie A. Harmon, Thomas H. Sanford, Amel Amalou, Michael Kassin, Nicole Varble, Maxime Blain, Dilara Long, Dima Hammoud, Ashkan Malayeri, Elizabeth Jones, Holger Roth, Ziyue Xu, Dong Yang, Andriy Myronenko, Victoria Anderson, Mona Flores, Francesca Patella, Maurizio Cariati, Kaku Tamura, Hirofumi Obinata, Hitoshi Mori, Ulas Bagci, Daguang Xu, Hayet Amalou, Robert Suh, Gianpaolo Carrafiello, Baris Turkbey, Bradford J. Wood.<\/li>Thanks for leadership support to:\u00a0 John Gallin, Steve Holland, Cliff Lane, Bruce Tromberg, Tom Misteli, Bill Dahut.<\/li>Supported by the NIH Center for Interventional Oncology and the NIH Intramural Targeted Anti-COVID-19 (ITAC) Program.<\/li><\/ul>TCIA COVID-19 Datasets<\/h3>Additional datasets and information about TCIA efforts to support COVID-19 research can be found\u00a0here<\/a>.<\/p>","collection_funding":"","hide_from_browse_table":"0","program":["COVID-19"],"_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/42049","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\/42049\/revisions"}],"predecessor-version":[{"id":47527,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/42049\/revisions\/47527"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media\/6917"}],"wp:attachment":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media?parent=42049"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
The Imaging AI in COVID team would like to acknowledge the following individuals who supported this multi-disciplinary multi-national team effort:<\/p>
Additional datasets and information about TCIA efforts to support COVID-19 research can be found\u00a0here<\/a>.<\/p>","collection_funding":"","hide_from_browse_table":"0","program":["COVID-19"],"_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/42049","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\/42049\/revisions"}],"predecessor-version":[{"id":47527,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/42049\/revisions\/47527"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media\/6917"}],"wp:attachment":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media?parent=42049"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}