{"id":46301,"date":"2023-11-20T06:05:51","date_gmt":"2023-11-20T12:05:51","guid":{"rendered":"https:\/\/stage.cancerimagingarchive.net\/analysis-result\/saros\/"},"modified":"2025-03-20T13:40:26","modified_gmt":"2025-03-20T18:40:26","slug":"saros","status":"publish","type":"tcia_analysis_result","link":"https:\/\/stage.cancerimagingarchive.net\/analysis-result\/saros\/","title":{"rendered":"SAROS"},"featured_media":9179,"template":"","class_list":["post-46301","tcia_analysis_result","type-tcia_analysis_result","status-publish","has-post-thumbnail"],"cancer_types":["Adenocarcinoma","Breast","Corpus Endometrial Carcinoma","COVID-19(non-cancer)","Cutaneous Melanoma","Ductal Adenocarcinoma","Head and Neck Carcinomas","Head and Neck Squamous Cell Carcinoma","Healthy Controls (non-cancer)","Kidney Cancer","Liver Hepatocellular Carcinoma","Lung Adenocarcinoma","Lung Cancer","Lung Squamous Cell Carcinoma","Melanoma","Non-small Cell Lung Cancer","Soft-tissue Sarcoma","Squamous Cell Carcinoma","Stomach Adenocarcinoma","Uterine Corpus Endometrial Carcinoma"],"citations":[46287,9225,48751],"result_doi":"10.25737\/SZ96-ZG60","result_download_info":"","result_downloads":[46289,46291],"version_change_log_archived":"Version 1 (Current): Updated 2023\/09\/28\r\nData TypeDownload all or Query\/FilterLicenseSAROS Segmentations (NIfTI, 140 MB)\r\n Download\u00a0\r\n(Download requires Aspera plugin)CC BY 4.0Segmentation Information Spreadsheet (CSV, 176 KB)\r\n Download\u00a0\r\nCC BY 4.0","versions":[48269],"additional_resources":"<ul>\r\n \t<li>A script to download and resample the images in GitHub repository: <a href=\"https:\/\/github.com\/UMEssen\/saros-dataset\">https:\/\/github.com\/UMEssen\/saros-dataset<\/a><\/li>\r\n<\/ul>","cancer_locations":["Breast","Chest","Extremities","Head-Neck","Kidney","Liver","Lung","Pancreas","Skin","Stomach","Uterus"],"publications_related":"","result_page_accessibility":"Limited","detailed_description":"","publications_using":"TCIA maintains\u00a0<a href=\"https:\/\/www.cancerimagingarchive.net\/publications\/\">a list of publications<\/a> which leverage TCIA data. If you have a manuscript you'd like to add please <a href=\"http:\/\/www.cancerimagingarchive.net\/support\/\">contact TCIA's Helpdesk<\/a>.","result_title":"SAROS - A large, heterogeneous, and sparsely annotated segmentation dataset on CT imaging data","species":["Human"],"version_number":"2","date_updated":"2024-03-07","related_collections":[41269,45043,41469,42977,41365,43005,42151,44097,44257,41425,44377,44599,44129,41233,41419,42503,43315,44929,42171,42371,44041,45161,43015,41219,43071,43955,43691],"result_short_title":"SAROS","subjects":"882","related_analysis_results":false,"result_browse_title":"SAROS - A large, heterogeneous, and sparsely annotated segmentation dataset on CT imaging data (SAROS)","supporting_data":["Segmentations"],"version_change_log":"The segmentations of 91 cases were updated to improve the segmentation quality. In some cases, some bones (mostly the ribs) were incorrectly annotated as \u201cmuscle\u201d. These mistakes were revised and the segmentation accuracy of these areas was improved.","collections":"Below is a list of the Collections used in these analyses.\r\n\r\nSome data in this collection contains images that could potentially be used to reconstruct a human face. To safeguard the privacy of participants, users must sign and submit a <a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/4556915\/TCIA%20Restricted%20License%2020220519.pdf?version=1&amp;modificationDate=1652964581655&amp;api=v2\">TCIA Restricted License Agreement<\/a> to <a href=\"mailto:help@cancerimagingarchive.net\">help@cancerimagingarchive.net<\/a> before accessing the data.\r\n<table><colgroup> <col \/> <col \/> <col \/><\/colgroup>\r\n<tbody>\r\n<tr>\r\n<th>Source Data<\/th>\r\n<th>Download all or Query\/Filter<\/th>\r\n<th>License<\/th>\r\n<\/tr>\r\n<tr>\r\n<td>Source Images (DICOM, 29.7 GB)\r\n\r\n<a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.JQEJZZNG\">ACRIN-HNSCC-FDG-PET\/CT<\/a> (48), <a href=\"https:\/\/doi.org\/10.7937\/tcia.2019.1ae0qtcu\">Anti-PD-1_MELANOMA<\/a> (2), <a href=\"https:\/\/doi.org\/10.7937\/k9\/tcia.2020.a8sh-7363\">HNSCC<\/a> (17), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2015.7AKGJUPZ\">Head-Neck Cetuximab<\/a> (12), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2015.K0F5CGLI\">QIN-HEADNECK<\/a> (94), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.LXKQ47MS\">TCGA-HNSC<\/a> (1)<\/td>\r\n<td>\r\n<div>\r\n\r\n<a href=\"\/wp-content\/uploads\/SAROS_SourceImages_TCIARestricted_9-12-2023.tcia\" download=\"SAROS_SourceImages_TCIARestricted_9-12-2023.tcia\"><button><i><\/i> Download<\/button><\/a>\r\n(Download requires <a href=\"https:\/\/wiki.cancerimagingarchive.net\/display\/NBIA\/Downloading+TCIA+Images\">NBIA Data Retriever<\/a>)\r\n\r\n<\/div><\/td>\r\n<td>\r\n<div>\r\n\r\n<a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/4556915\/TCIA%20Restricted%20License%2020220519.pdf?version=1&amp;modificationDate=1652964581655&amp;api=v2\">TCIA Restricted<\/a>\r\n\r\n<\/div><\/td>\r\n<\/tr>\r\n<tr>\r\n<td>Source Images (DICOM, 66.9 GB)\r\n\r\n<a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.ol20zmxg\">ACRIN-FLT-Breast<\/a> (32), <a href=\"https:\/\/doi.org\/10.7937\/tcia.2019.30ilqfcl\">ACRIN-NSCLC-FDG-PET<\/a> (129), <a href=\"https:\/\/doi.org\/10.7937\/tcia.2019.zjjwb9ip\">Anti-PD-1_Lung<\/a> (12), <a href=\"https:\/\/doi.org\/10.7937\/TCIA.2019.IX49E8NX\">C4KC-KiTS<\/a> (175), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2018.ODU24GZE\">CPTAC-CM<\/a> (1), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2018.6EMUB5L2\">CPTAC-LSCC<\/a> (3), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2018.PAT12TBS\">CPTAC-LUAD<\/a> (1), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2018.SC20FO18\">CPTAC-PDA<\/a> (8), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2018.3R3JUISW\">CPTAC-UCEC<\/a> (26), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2015.LO9QL9SX\">LIDC-IDRI<\/a> (133), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.7hs46erv\">NSCLC Radiogenomics<\/a> (7), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.tNB1kqBU\">Pancreas-CT<\/a> (58), <a href=\"http:\/\/doi.org\/10.7937\/K9\/TCIA.2015.7GO2GSKS\">Soft-tissue-Sarcoma<\/a> (6), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.IMMQW8UQ\">TCGA-LIHC<\/a> (33), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.JGNIHEP5\">TCGA-LUAD<\/a> (2), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.TYGKKFMQ\">TCGA-LUSC<\/a> (3), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.GDHL9KIM\">TCGA-STAD<\/a> (2), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.GKJ0ZWAC\">TCGA-UCEC<\/a> (1)<\/td>\r\n<td>\r\n<div>\r\n\r\n<a href=\"\/wp-content\/uploads\/SAROS_SourceImages_CCby30_9-12-2023.tcia\" download=\"SAROS_SourceImages_CCby30_9-12-2023.tcia\"><button><i><\/i> Download<\/button><\/a>\r\n(Download requires <a href=\"https:\/\/wiki.cancerimagingarchive.net\/display\/NBIA\/Downloading+TCIA+Images\">NBIA Data Retriever<\/a>)\r\n\r\n<\/div><\/td>\r\n<td>\r\n<div>\r\n\r\n<a href=\"https:\/\/creativecommons.org\/licenses\/by\/3.0\/\">CC BY 3.0<\/a>\r\n\r\n<\/div><\/td>\r\n<\/tr>\r\n<tr>\r\n<td>Source Images (DICOM, 4.63 GB)\r\n\r\n<a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2015.PF0M9REI\">NSCLC-Radiomics<\/a> (56), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2015.L4FRET6Z\">NSCLC-Radiomics-Genomics<\/a> (20)<\/td>\r\n<td>\r\n<div>\r\n\r\n<a href=\"\/wp-content\/uploads\/SAROS_SourceImages_CCbyNC30_9-12-2023.tcia\" download=\"SAROS_SourceImages_CCbyNC30_9-12-2023.tcia\"><button><i><\/i> Download<\/button><\/a>\r\n(Download requires <a href=\"https:\/\/wiki.cancerimagingarchive.net\/display\/NBIA\/Downloading+TCIA+Images\">NBIA Data Retriever<\/a>)\r\n\r\n<\/div><\/td>\r\n<td>\r\n<div>\r\n\r\n<a href=\"https:\/\/creativecommons.org\/licenses\/by-nc\/3.0\/\">CC BY-NC 3.0<\/a>\r\n\r\n<\/div><\/td>\r\n<\/tr>\r\n<tr>\r\n<td>Source Images (DICOM, 1.40 GB)\r\n\r\n<a href=\"https:\/\/doi.org\/10.7937\/TCIA.BBAG-2923\">COVID-19-NY-SBU<\/a> (1), <a href=\"https:\/\/doi.org\/10.7937\/TCIA.2020.NNC2-0461\">Lung-PET-CT-Dx<\/a> (17)<\/td>\r\n<td>\r\n<div>\r\n\r\n<a href=\"\/wp-content\/uploads\/SAROS_SourceImages_CCby40_9-12-2023.tcia\" download=\"SAROS_SourceImages_CCby40_9-12-2023.tcia\"><button><i><\/i> Download<\/button><\/a>\r\n(Download requires <a href=\"https:\/\/wiki.cancerimagingarchive.net\/display\/NBIA\/Downloading+TCIA+Images\">NBIA Data Retriever<\/a>)\r\n\r\n<\/div><\/td>\r\n<td>\r\n<div>\r\n\r\n<a href=\"https:\/\/creativecommons.org\/licenses\/by\/4.0\/\">CC BY 4.0<\/a>\r\n\r\n<\/div><\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<ul>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.ol20zmxg\">ACRIN-FLT-Breast<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.JQEJZZNG\">ACRIN-HNSCC-FDG-PET\/CT<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/tcia.2019.30ilqfcl\">ACRIN-NSCLC-FDG-PET<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/tcia.2019.zjjwb9ip\">Anti-PD-1_Lung<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/tcia.2019.1ae0qtcu\">Anti-PD-1_MELANOMA<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/TCIA.2019.IX49E8NX\">C4KC-KiTS<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/TCIA.BBAG-2923\">COVID-19-NY-SBU<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2018.ODU24GZE\">CPTAC-CM<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2018.6EMUB5L2\">CPTAC-LSCC<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2018.PAT12TBS\">CPTAC-LUAD<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2018.SC20FO18\">CPTAC-PDA<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2018.3R3JUISW\">CPTAC-UCEC<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/k9\/tcia.2020.a8sh-7363\">HNSCC<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2015.7AKGJUPZ\">Head-Neck Cetuximab<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2015.LO9QL9SX\">LIDC-IDRI<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/TCIA.2020.NNC2-0461\">Lung-PET-CT-Dx<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.7hs46erv\">NSCLC Radiogenomics<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2015.PF0M9REI\">NSCLC-Radiomics<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2015.L4FRET6Z\">NSCLC-Radiomics-Genomics<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.tNB1kqBU\">Pancreas-CT<\/a><\/li>\r\n \t<li>\u00a0<a href=\"http:\/\/doi.org\/10.7937\/K9\/TCIA.2015.7GO2GSKS\">Soft-tissue-Sarcoma<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2015.K0F5CGLI\">QIN-HEADNECK<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.LXKQ47MS\">TCGA-HNSC<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.IMMQW8UQ\">TCGA-LIHC<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.JGNIHEP5\">TCGA-LUAD<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.TYGKKFMQ\">TCGA-LUSC<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.GDHL9KIM\">TCGA-STAD<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.GKJ0ZWAC\">TCGA-UCEC<\/a><\/li>\r\n<\/ul>","result_summary":"Sparsely Annotated Region and Organ Segmentation (SAROS) contributes a large heterogeneous semantic segmentation annotation dataset for existing CT imaging cases on TCIA. The goal of this dataset is to provide high-quality annotations for building body composition analysis tools (References: <a title=\"https:\/\/gcc02.safelinks.protection.outlook.com\/?url=https%3A%2F%2Fdoi.org%2F10.1007%2Fs00330-020-07147-3&amp;data=05%7C02%7Cfevriersullivby%40mail.nih.gov%7Ccf99ac5db5e949ee51fb08dc4a95f4a2%7C14b77578977342d58507251ca2dc2b06%7C0%7C0%7C638467255110189608%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&amp;sdata=QOvbuO2U0O7w5zEZvHOUr7FVd9x2WJoCI9BcuPsNUe0%3D&amp;reserved=0\" href=\"https:\/\/gcc02.safelinks.protection.outlook.com\/?url=https%3A%2F%2Fdoi.org%2F10.1007%2Fs00330-020-07147-3&amp;data=05%7C02%7Cfevriersullivby%40mail.nih.gov%7Ccf99ac5db5e949ee51fb08dc4a95f4a2%7C14b77578977342d58507251ca2dc2b06%7C0%7C0%7C638467255110189608%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&amp;sdata=QOvbuO2U0O7w5zEZvHOUr7FVd9x2WJoCI9BcuPsNUe0%3D&amp;reserved=0\">Koitka 2020<\/a>\u00a0and <a title=\"https:\/\/gcc02.safelinks.protection.outlook.com\/?url=https%3A%2F%2Fdoi.org%2F10.1097%2Frli.0000000000001040&amp;data=05%7C02%7Cfevriersullivby%40mail.nih.gov%7Ccf99ac5db5e949ee51fb08dc4a95f4a2%7C14b77578977342d58507251ca2dc2b06%7C0%7C0%7C638467255110198944%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&amp;sdata=aXBg2QHEz%2BeNs%2FdWPC%2FoEaGOiO2iMBben7gmcXpGtu0%3D&amp;reserved=0\" href=\"https:\/\/gcc02.safelinks.protection.outlook.com\/?url=https%3A%2F%2Fdoi.org%2F10.1097%2Frli.0000000000001040&amp;data=05%7C02%7Cfevriersullivby%40mail.nih.gov%7Ccf99ac5db5e949ee51fb08dc4a95f4a2%7C14b77578977342d58507251ca2dc2b06%7C0%7C0%7C638467255110198944%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&amp;sdata=aXBg2QHEz%2BeNs%2FdWPC%2FoEaGOiO2iMBben7gmcXpGtu0%3D&amp;reserved=0\">Haubold 2023<\/a>). Existing in-house segmentation models were employed to generate annotation candidates on randomly selected cases. All generated annotations were manually reviewed and corrected by medical residents and students on every fifth axial slice while other slices were set to an ignore label (numeric value 255).\r\n\r\n900 CT series from 882 patients were randomly selected from the following TCIA collections (number of CTs per collection in parenthesis):\u00a0 <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.ol20zmxg\">ACRIN-FLT-Breast<\/a> (32), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.JQEJZZNG\">ACRIN-HNSCC-FDG-PET\/CT<\/a> (48), <a href=\"https:\/\/doi.org\/10.7937\/tcia.2019.30ilqfcl\">ACRIN-NSCLC-FDG-PET<\/a> (129), <a href=\"https:\/\/doi.org\/10.7937\/tcia.2019.zjjwb9ip\">Anti-PD-1_Lung<\/a> (12), <a href=\"https:\/\/doi.org\/10.7937\/tcia.2019.1ae0qtcu\">Anti-PD-1_MELANOMA<\/a> (2), <a href=\"https:\/\/doi.org\/10.7937\/TCIA.2019.IX49E8NX\">C4KC-KiTS<\/a> (175), <a href=\"https:\/\/doi.org\/10.7937\/TCIA.BBAG-2923\">COVID-19-NY-SBU<\/a> (1), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2018.ODU24GZE\">CPTAC-CM<\/a> (1), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2018.6EMUB5L2\">CPTAC-LSCC<\/a> (3), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2018.PAT12TBS\">CPTAC-LUAD<\/a> (1), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2018.SC20FO18\">CPTAC-PDA<\/a> (8), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2018.3R3JUISW\">CPTAC-UCEC<\/a> (26), <a href=\"https:\/\/doi.org\/10.7937\/k9\/tcia.2020.a8sh-7363\">HNSCC<\/a> (17), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2015.7AKGJUPZ\">Head-Neck Cetuximab<\/a> (12), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2015.LO9QL9SX\">LIDC-IDRI<\/a> (133), <a href=\"https:\/\/doi.org\/10.7937\/TCIA.2020.NNC2-0461\">Lung-PET-CT-Dx<\/a> (17), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.7hs46erv\">NSCLC Radiogenomics<\/a> (7), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2015.PF0M9REI\">NSCLC-Radiomics<\/a> (56), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2015.L4FRET6Z\">NSCLC-Radiomics-Genomics<\/a> (20), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.tNB1kqBU\">Pancreas-CT<\/a> (58), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2015.K0F5CGLI\">QIN-HEADNECK<\/a> (94), <a href=\"http:\/\/doi.org\/10.7937\/K9\/TCIA.2015.7GO2GSKS\">Soft-tissue-Sarcoma<\/a> (6), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.LXKQ47MS\">TCGA-HNSC<\/a> (1), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.IMMQW8UQ\">TCGA-LIHC<\/a> (33), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.JGNIHEP5\">TCGA-LUAD<\/a> (2), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.TYGKKFMQ\">TCGA-LUSC<\/a> (3), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.GDHL9KIM\">TCGA-STAD<\/a> (2), <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.GKJ0ZWAC\">TCGA-UCEC<\/a> (1).\r\n\r\nA script to download and resample the images is provided in our GitHub repository: <a href=\"https:\/\/github.com\/UMEssen\/saros-dataset\">https:\/\/github.com\/UMEssen\/saros-dataset<\/a>\r\n\r\nThe annotations are provided in NIfTI format and were performed on 5mm slice thickness. The annotation files define foreground labels on the same axial slices and match pixel-perfect. In total, 13 semantic body regions and 6 body part\u00a0<strong>labels were annotated with an index that corresponds to a numeric value in the segmentation file.\u00a0<\/strong>\r\n<h3>Body Regions<\/h3>\r\n<ol>\r\n \t<li>Subcutaneous Tissue<\/li>\r\n \t<li>Muscle<\/li>\r\n \t<li>Abdominal Cavity<\/li>\r\n \t<li>Thoracic Cavity<\/li>\r\n \t<li>Bones<\/li>\r\n \t<li>Parotid Glands<\/li>\r\n \t<li>Pericardium<\/li>\r\n \t<li>Breast Implant<\/li>\r\n \t<li>Mediastinum<\/li>\r\n \t<li>Brain<\/li>\r\n \t<li>Spinal Cord<\/li>\r\n \t<li>Thyroid Glands<\/li>\r\n \t<li>Submandibular Glands<\/li>\r\n<\/ol>\r\n<h3>Body Parts<\/h3>\r\n<ol>\r\n \t<li>Torso<\/li>\r\n \t<li>Head<\/li>\r\n \t<li>Right Leg<\/li>\r\n \t<li>Left Leg<\/li>\r\n \t<li>Right Arm<\/li>\r\n \t<li>Left Arm<\/li>\r\n<\/ol>\r\nThe labels which were modified or require further commentary are listed and explained below:\r\n<ul>\r\n \t<li>Subcutaneous Adipose Tissue: The cutis was included into this label due to its limited differentiation in 5mm-CT.<\/li>\r\n \t<li>Muscle: All muscular tissue was segmented contiguously and not separated into single muscles. Thus, fascias and intermuscular fat were included into the label. Inter- and intramuscular fat is subtracted automatically in the process.<\/li>\r\n \t<li>Abdominal Cavity: This label includes the pelvis. The label does not separate between the positional relationships of the peritoneum.<\/li>\r\n \t<li>Mediastinum:\u00a0<a href=\"https:\/\/itmig.org\/\">The International Thymic Malignancy Group<\/a>\u00a0(ITMIG) scheme was used for the segmentation guidelines.<\/li>\r\n \t<li>Head + Neck: The neck is confined by the base of the trapezius muscle.<\/li>\r\n \t<li>Right + Left Leg: The legs are separated from the torso by the line between the two lowest points of the Rami ossa pubis.<\/li>\r\n \t<li>Right + Left Arm: The arms are separated from the torso by the diagonal between the most lateral point of the acromion and the tuberculum infraglenoidale.<\/li>\r\n<\/ul>\r\nFor reproducibility on downstream tasks, five cross-validation folds and a test set were pre-defined and are described in the provided spreadsheet. Segmentation was conducted strictly in accordance with anatomical guidelines and only modified if required for the gain of segmentation efficiency.","collection_downloads":[46293,46295,46297,46299],"result_featured_image":{"ID":"9179","post_author":"29","post_date":"2023-09-14 01:08:37","post_date_gmt":"2023-09-14 06:08:37","post_content":"","post_title":"SAROS-Collection-Photo","post_excerpt":"","post_status":"inherit","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"saros-collection-photo","to_ping":"","pinged":"","post_modified":"2023-11-20 06:05:53","post_modified_gmt":"2023-11-20 12:05:53","post_content_filtered":"","post_parent":"46301","guid":"https:\/\/stage.cancerimagingarchive.net\/wp-content\/uploads\/SAROS-Collection-Photo.png","menu_order":"0","post_type":"attachment","post_mime_type":"image\/png","comment_count":"0","pod_item_id":"9179"},"result_acknowledgements":"To the entire annotation lab team at the Institute for Artificial Intelligence in Medicine (IKIM, University Hospital Essen), we express our profound gratitude for your meticulous efforts in data segmentation. Your dedication ensures accuracy and efficiency, paving the way for this collection. Thank you for your invaluable contribution.\r\n\r\nTo all collections that shared their data and made it possible that we could prepare the segmentations: thank you! Your contributions made it possible to provide an open available segmentation dataset for CT based body composition analysis.","hide_from_browse_table":"0","program":["Community"],"_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/analysis-results\/46301","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/analysis-results"}],"about":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/types\/tcia_analysis_result"}],"version-history":[{"count":1,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/analysis-results\/46301\/revisions"}],"predecessor-version":[{"id":47253,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/analysis-results\/46301\/revisions\/47253"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media\/9179"}],"wp:attachment":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media?parent=46301"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}