{"id":5530,"date":"2023-09-04T03:06:08","date_gmt":"2023-09-04T03:06:08","guid":{"rendered":"https:\/\/cm.vastapps.dev\/tcia-collection\/ct-lymph-nodes\/"},"modified":"2023-09-13T11:56:02","modified_gmt":"2023-09-13T11:56:02","slug":"ct-lymph-nodes","status":"publish","type":"tcia_collection","link":"https:\/\/cm.vastapps.dev\/tcia-collection\/ct-lymph-nodes\/","title":{"rendered":"CT-LYMPH-NODES"},"featured_media":0,"template":"","citation-tax":[],"cancer_types":["Lymphadenopathy (non-cancer)"],"citations":[4334,4335,2925],"collection_doi":"10.7937\/K9\/TCIA.2015.AQIIDCNM","collection_download_info":"Click the Versions tab for more info about data releases.","collection_downloads":[4888,4889,4890,4891],"full_export":"<h2 id=\"Anew2.5DrepresentationforlymphnodedetectioninCT(CTLymphNodes)-Summary\">Summary<\/h2><p>This collection consists of Computed Tomography (CT) images of the mediastinum and abdomen in which lymph node positions are marked by radiologists at the National Institutes of Health, Clinical Center. Radiologists at the <em> <a href=\"https:\/\/clinicalcenter.nih.gov\/meet-our-doctors\/rsummers.html\" class=\"external-link\" rel=\"nofollow\"> <strong>I<\/strong><strong>maging Biomarkers and Computer-Aided Diagnosis Laboratory<\/strong> <\/a> <\/em> labeled a total of 388 mediastinal lymph nodes in CT images of 90 patients and a total of 595 abdominal lymph nodes in 86 patients.<\/p><p>The collection is aimed at the medical image computing community for developing and assessing computer-aided detection methods. Automated detection of lymph nodes can be an important clinical diagnostic tool but is very challenging due to the low contrast of surrounding structures in CT and to their varying sizes, poses, shapes and sparsely distributed locations. This data set is made available to make direct comparison to other detection methods in order to advance the state of the art.<\/p><h3 id=\"Anew2.5DrepresentationforlymphnodedetectioninCT(CTLymphNodes)-Acknowledgements\">Acknowledgements<\/h3><ul><li>We would like to acknowledge the individuals and institutions that have provided data for this collection: National Institutes of Health, Bethesda MD. <span style=\"background-color: rgb(255,255,255);letter-spacing: 0.0px;\">\u00a0 Special thanks to <\/span><span style=\"background-color: rgb(255,255,255);letter-spacing: 0.0px;\"> <strong>Dr. Holger R. Roth <\/strong>and<strong> Dr. Ronald Summers, <em> <strong> <a href=\"https:\/\/clinicalcenter.nih.gov\/meet-our-doctors\/rsummers.html\" class=\"external-link\" rel=\"nofollow\">Imaging Biomarkers and Computer-Aided Diagnosis Laboratory<\/a> <\/strong> <\/em> <span style=\"color: rgb(71,92,142);\">, <\/span> <\/strong> <span style=\"color: rgb(71,92,142);\">Grant Magnuson Clinical Center<\/span> <\/span><span style=\"background-color: rgb(255,255,255);letter-spacing: 0.0px;\">.<\/span><\/li><li><span style=\"background-color: rgb(255,255,255);letter-spacing: 0.0px;\"><span style=\"color: rgb(0,0,0);\">Conversion of the segmentations into DICOM SEG representation was completed by Cosmin Ciausu using dcmqi (<\/span><a title=\"Original URL: https:\/\/github.com\/QIICR\/dcmqi. Click or tap if you trust this link.\" href=\"https:\/\/gcc02.safelinks.protection.outlook.com\/?url=https%3A%2F%2Fgithub.com%2FQIICR%2Fdcmqi&amp;data=05%7C01%7Ckirbyju%40mail.nih.gov%7Cf91c2d574a344e8d972d08db3217a378%7C14b77578977342d58507251ca2dc2b06%7C0%7C0%7C638158849339372035%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&amp;sdata=tHPQabx6hJ0Nry7BEjckaZMsuQ3H4%2FhNT5ijscuINcs%3D&amp;reserved=0\" style=\"text-align: left;\" class=\"external-link\" rel=\"nofollow\">https:\/\/github.com\/QIICR\/dcmqi<\/a><span style=\"color: rgb(0,0,0);\">), assisted by Andrey Fedorov, David Clunie, and other members of the <a href=\"https:\/\/portal.imaging.datacommons.cancer.gov\/\" class=\"external-link\" rel=\"nofollow\">NCI Imaging Data Commons<\/a><\/span><span style=\"color: rgb(0,0,0);\">\u00a0team.\u00a0<\/span><span style=\"color: rgb(33,37,41);\">NCI Imaging Data Commons consortium is supported by the contract number 19X037Q from Leidos Biomedical Research under Task Order HHSN26100071 under Contract Number HHSN261201500003l from NCI.<\/span><\/span><\/li><\/ul><p><br\/><\/p><div class=\"tab-style-builtin\"><div class=\"localtabs-macro\"><div class=\"aui-tabs horizontal-tabs\" role=\"application\" data-aui-responsive=\"true\"><ul class=\"tabs-menu\"><li class=\"menu-item bv-localtab  active-tab \"><a href=\"#19726546fcb14b04d2494090ab696ba899c8d70c\"><strong>Data Access<\/strong><\/a> <\/li><li class=\"menu-item bv-localtab \"><a href=\"#19726546ae04e7253c0b4a31864e981353acb4b0\"><strong>Detailed Description<\/strong><\/a> <\/li><li class=\"menu-item bv-localtab \"><a href=\"#19726546c22ed4a0ef364befa4e3d70996b3866d\"><strong>Citations &amp; Data Usage Policy<\/strong><\/a> <\/li><li class=\"menu-item bv-localtab \"><a href=\"#1972654624f20fe565d14a77a20bd8c270e8bdbb\"><strong>Versions<\/strong><\/a> <\/li><\/ul><div class=\"tabs-pane  active-pane \" id=\"19726546fcb14b04d2494090ab696ba899c8d70c\" active=\"true\" name=\"Data Access\" ><h3 id=\"Anew2.5DrepresentationforlymphnodedetectioninCT(CTLymphNodes)-DataAccess\">Data Access<\/h3><div class=\"table-wrap\"><table class=\"wrapped relative-table confluenceTable\" style=\"width: 46.8966%;\"><colgroup><col style=\"width: 37.0078%;\"\/><col style=\"width: 37.7566%;\"\/><col style=\"width: 25.1756%;\"\/><\/colgroup><tbody><tr><th class=\"confluenceTh\">Data Type<\/th><th class=\"confluenceTh\">Download all or Query\/Filter<\/th><th class=\"confluenceTh\">License<\/th><\/tr><tr><td class=\"confluenceTd\">Images, Segmentations (DICOM, 58.4 GB)<\/td><td class=\"confluenceTd\"><div class=\"content-wrapper\"><p>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/19726546\/TCIA_CT_Lymph_Nodes_03-31-2023.tcia?api=v2\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/www.cancerimagingarchive.net\/nbia-search\/?CollectionCriteria=CT%20Lymph%20Nodes\" class=\"external-link\" rel=\"nofollow\"><button class=\"tcia-btn tcia-search-color\"><i class=\"fa fa-search\" \/> Search<\/button><\/a>\u00a0\n<\/span><\/p><\/div><p>(Download requires\u00a0the\u00a0<a href=\"https:\/\/wiki.cancerimagingarchive.net\/display\/NBIA\/Downloading+TCIA+Images\" rel=\"nofollow\">NBIA Data Retriever<\/a>)<\/p><\/td><td class=\"confluenceTd\"><div class=\"content-wrapper\"><p>\n<a href=\"https:\/\/creativecommons.org\/licenses\/by\/3.0\/\" class=\"external-link\" rel=\"nofollow\">CC BY 3.0<\/a><\/p><\/div><\/td><\/tr><tr><td class=\"confluenceTd\">Med ABD Lymph Annotations (ZIP)<\/td><td class=\"confluenceTd\"><div class=\"content-wrapper\"><p>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/19726546\/MED_ABD_LYMPH_ANNOTATIONS.zip?version=1&amp;modificationDate=1435166807156&amp;api=v2\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span><\/p><\/div><\/td><td class=\"confluenceTd\"><div class=\"content-wrapper\"><p>\n<a href=\"https:\/\/creativecommons.org\/licenses\/by\/3.0\/\" class=\"external-link\" rel=\"nofollow\">CC BY 3.0<\/a><\/p><\/div><\/td><\/tr><tr><td class=\"confluenceTd\"><span>Med Lymph Candidate Nodes (ZIP)<\/span><\/td><td class=\"confluenceTd\"><div class=\"content-wrapper\"><p>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/19726546\/MED_ABD_LYMPH_CANDIDATES.zip?version=1&amp;modificationDate=1442245247654&amp;api=v2\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span><\/p><\/div><\/td><td class=\"confluenceTd\"><div class=\"content-wrapper\"><p>\n<a href=\"https:\/\/creativecommons.org\/licenses\/by\/3.0\/\" class=\"external-link\" rel=\"nofollow\">CC BY 3.0<\/a><\/p><\/div><\/td><\/tr><tr><td class=\"confluenceTd\"><span>Med ABD Lymph Masks (<\/span> <span>ZIP)<\/span><\/td><td class=\"confluenceTd\"><div class=\"content-wrapper\"><p>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/19726546\/MED_ABD_LYMPH_MASKS.zip?version=1&amp;modificationDate=1449684916503&amp;api=v2\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span><\/p><\/div><\/td><td class=\"confluenceTd\"><div class=\"content-wrapper\"><p>\n<a href=\"https:\/\/creativecommons.org\/licenses\/by\/3.0\/\" class=\"external-link\" rel=\"nofollow\">CC BY 3.0<\/a><\/p><\/div><\/td><\/tr><\/tbody><\/table><\/div><p>Click the Versions tab for more info about data releases.<\/p><h3 style=\"\" id=\"Anew2.5DrepresentationforlymphnodedetectioninCT(CTLymphNodes)-AdditionalResourcesforthisDataset\">Additional Resources for this Dataset<\/h3><p><span style=\"color: rgb(23,43,77);\">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.<\/span><\/p><ul><li class=\"auto-cursor-target\"><a href=\"https:\/\/portal.imaging.datacommons.cancer.gov\/explore\/filters\/?collection_id=ct_lymph_nodes\" class=\"external-link\" rel=\"nofollow\">Imaging Data Commons (IDC)<\/a> (Imaging Data)<\/li><\/ul><\/div><div class=\"tabs-pane \" id=\"19726546ae04e7253c0b4a31864e981353acb4b0\" name=\"Detailed Description\" ><h3 id=\"Anew2.5DrepresentationforlymphnodedetectioninCT(CTLymphNodes)-DetailedDescription\">Detailed Description<\/h3><div class=\"table-wrap\"><table class=\"wrapped confluenceTable\"><tbody><tr><th class=\"confluenceTh\"><div class=\"tablesorter-header-inner\"><p>Collection Statistics<\/p><\/div><\/th><th class=\"confluenceTh\"><div class=\"tablesorter-header-inner\"><p><br\/><\/p><\/div><\/th><\/tr><tr><td class=\"confluenceTd\"><p>Modalities<\/p><\/td><td class=\"confluenceTd\"><p>CT, SEG<\/p><\/td><\/tr><tr><td class=\"confluenceTd\"><p>Number of Participants<\/p><\/td><td class=\"confluenceTd\"><p>176<\/p><\/td><\/tr><tr><td class=\"confluenceTd\"><p>Number of Studies<\/p><\/td><td class=\"confluenceTd\"><p>176<\/p><\/td><\/tr><tr><td class=\"confluenceTd\"><p>Number of Series<\/p><\/td><td class=\"confluenceTd\"><p>352<\/p><\/td><\/tr><tr><td class=\"confluenceTd\"><p>Number of Images<\/p><\/td><td class=\"confluenceTd\"><p>110,179<\/p><\/td><\/tr><tr><td colspan=\"1\" class=\"confluenceTd\">Images Size (GB)<\/td><td colspan=\"1\" class=\"confluenceTd\"><p>58.4<\/p><\/td><\/tr><\/tbody><\/table><\/div><p><span>The DICOM files were created from volumetric images (Analyze and NifTI) using this from ITK: \u00a0<\/span> <strong> <em> <a href=\"http:\/\/www.itk.org\/Doxygen\/html\/Examples_2IO_2ImageReadDicomSeriesWrite_8cxx-example.html\" class=\"external-link\" rel=\"nofollow\">http:\/\/www.itk.org\/Doxygen\/html\/Examples_2IO_2ImageReadDicomSeriesWrite_8cxx-example.html<\/a>.<\/em> <\/strong><\/p><h3 id=\"Anew2.5DrepresentationforlymphnodedetectioninCT(CTLymphNodes)-Annotationfiles\">Annotation files<\/h3><p>\u00a0<strong> <em> <a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/19726546\/MED_ABD_LYMPH_ANNOTATIONS.zip?version=1&amp;modificationDate=1435166807156&amp;api=v2\" rel=\"nofollow\">MED_ABD_LYMPH_ANNOTATIONS.zip<\/a> <\/em> <\/strong>\u00a0(new 6\/24\/2015). The annotations include a folder for each case with text files of voxel indices, physical coordinates, size measurements and a\u00a0<a class=\"external-link\" href=\"http:\/\/mitk.org\/\" rel=\"nofollow\">MITK\u00a0<\/a>point set file (.mps), which can be visualized using the\u00a0<a href=\"http:\/\/mitk.org\/Download\" rel=\"nofollow\" class=\"external-link\">MITK workbench<\/a>\u00a0(Note: only release\u00a0<a title=\"MITK ReleaseNotes 2014.10\" href=\"http:\/\/mitk.org\/wiki\/MITK_ReleaseNotes_2014.10\" rel=\"nofollow\" class=\"external-link\">2014.10.0<\/a>\u00a0and later supports visualization of point set files using the &quot;point set interaction plugin&quot;). Abdominal size measurements\u00a0include the longest and shortest axis in axial view of a lymph node. The shortest axis is used for the RECIST criteria. The mediastinal set only includes the shortest axis.<\/p><h3 id=\"Anew2.5DrepresentationforlymphnodedetectioninCT(CTLymphNodes)-Mediastinalandabdominallymphnodes\"><span>Mediastinal and abdominal lymph nodes<\/span><\/h3><p>Computer-generated candidate detections for mediastinal and abdominal lymph nodes (produced by methods in [K. Cherry et al., SPIE Med. Img. 2014] and [J. Liu et al., SPIE Med. Img. 2014]]). \u00a0See attached:\u00a0<a style=\"line-height: 1.42857;\" href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/19726546\/MED_ABD_LYMPH_CANDIDATES.zip?version=1&amp;modificationDate=1442245247654&amp;api=v2\" rel=\"nofollow\">MED_ABD_LYMPH_CANDIDATES.zip<\/a>\u00a0(new 9\/14\/2015).<\/p><p><em> <strong> <a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/19726546\/MED_ABD_LYMPH_MASKS.zip?version=1&amp;modificationDate=1449684916503&amp;api=v2\" rel=\"nofollow\">MED_ABD_LYMPH_MASKS.zip<\/a>\u00a0<\/strong> <\/em>(new 12\/8\/2015): These files contain a compressed NifTI image (*.nii.gz) for each patient with manually traced lymph node segmentations. Note: these segmentation masks were produced independently to the centroid annotations in MED_ABD_LYMPH_ANNOTATIONS.zip. There is an overlapping set of lymph nodes marked in both files but the indexing does not align.\u00a0 On 3\/31\/2023 (version 5) a DICOM-SEG version of these data were added to the collection.<\/p><p>Please cite the following paper when using the segmentation masks:<\/p><p style=\"margin-left: 30.0px;\">A Seff, L Lu, A Barbu, H Roth, HC Shin, RM Summers. <strong>Leveraging Mid-Level Semantic Boundary Cues for Automated Lymph Node Detection<\/strong>. Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015, 53-61 (<a href=\"http:\/\/link.springer.com\/chapter\/10.1007\/978-3-319-24571-3_7\" class=\"external-link\" rel=\"nofollow\">http:\/\/link.springer.com\/chapter\/10.1007\/978-3-319-24571-3_7<\/a>)<\/p><\/div><div class=\"tabs-pane \" id=\"19726546c22ed4a0ef364befa4e3d70996b3866d\" name=\"Citations BITVOODOO_ANDamp; Data Usage Policy\" ><h3 id=\"Anew2.5DrepresentationforlymphnodedetectioninCT(CTLymphNodes)-Citations&amp;DataUsagePolicy\">Citations &amp; Data Usage Policy\u00a0<\/h3><p class=\"auto-cursor-target\">\n<p>\nUsers must abide by the <a href=\"https:\/\/wiki.cancerimagingarchive.net\/x\/c4hF\" class=\"external-link\" rel=\"nofollow\">TCIA Data Usage Policy and Restrictions<\/a>. Attribution should include references to the following citations:\n<\/p><\/p><div class=\"confluence-information-macro confluence-information-macro-information\"><p class=\"title\">Data Citation<\/p><span class=\"aui-icon aui-icon-small aui-iconfont-info confluence-information-macro-icon\"><\/span><div class=\"confluence-information-macro-body\"><p>Roth, H., Lu, L., Seff, A., Cherry, K. M., Hoffman, J., Wang, S., Liu, J., Turkbey, E., &amp; Summers, R. M. (2015). <strong>A new 2.5 D representation for lymph node detection in CT [Data set]<\/strong>. The Cancer Imaging Archive. <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2015.AQIIDCNM\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.7937\/K9\/TCIA.2015.AQIIDCNM<\/a><\/p><\/div><\/div><div class=\"confluence-information-macro confluence-information-macro-information\"><p class=\"title\">Publication Citation<\/p><span class=\"aui-icon aui-icon-small aui-iconfont-info confluence-information-macro-icon\"><\/span><div class=\"confluence-information-macro-body\"><p>Roth, H. R., Lu, L., Seff, A., Cherry, K. M., Hoffman, J., Wang, S., Liu, J., Turkbey, E., &amp; Summers, R. M. (2014). <strong>A New 2.5D Representation for Lymph Node Detection Using Random Sets of Deep Convolutional Neural Network Observations<\/strong>. In Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2014 (pp. 520\u2013527). Springer International Publishing. <a href=\"https:\/\/doi.org\/10.1007\/978-3-319-10404-1_65\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1007\/978-3-319-10404-1_65<\/a><\/p><\/div><\/div><div class=\"confluence-information-macro confluence-information-macro-information\"><p class=\"title\">TCIA Citation<\/p><span class=\"aui-icon aui-icon-small aui-iconfont-info confluence-information-macro-icon\"><\/span><div class=\"confluence-information-macro-body\"><p>Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., &amp; Prior, F. (2013). <strong>The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository<\/strong>. Journal of Digital Imaging, 26(6), 1045\u20131057. <a href=\"https:\/\/doi.org\/10.1007\/s10278-013-9622-7\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1007\/s10278-013-9622-7<\/a><\/p><\/div><\/div><h3 style=\"text-align: left;\" id=\"Anew2.5DrepresentationforlymphnodedetectioninCT(CTLymphNodes)-AdditionalPublicationResources:\">Additional Publication Resources:<\/h3><p style=\"text-align: left;\">The Collection <em>authors <\/em>suggest the below will give context to this dataset, please cite if you use them in your work:<\/p><ul><li style=\"text-align: left;\">Seff, A., Lu, L., Cherry, K.M., Roth, H.R., Liu, J., Wang, S., Hoffman, J., Turkbey, E.B., &amp; Summers, R.M. <strong>2D view aggregation for lymph node detection using a shallow hierarchy of linear classifiers<\/strong>.<em>\u00a0<\/em>Medical Image Computing and Computer-Assisted Intervention--MICCAI 2014, p544-552, 2014. (<a href=\"http:\/\/arxiv.org\/abs\/1408.3337\" class=\"external-link\" rel=\"nofollow\">http:\/\/arxiv.org\/abs\/1408.3337<\/a>)<\/li><li><span style=\"color: rgb(255,0,0);\">Please cite the following paper when using the segmentation masks:\u00a0 <\/span>Seff, A., Lu, L., Barbu, A., Roth, H., Shin, H.-C., &amp; Summers, R. M. (2015).<strong style=\"letter-spacing: 0.0px;\"> Leveraging Mid-Level Semantic Boundary Cues for Automated Lymph Node Detection.<\/strong><span style=\"letter-spacing: 0.0px;\"> In Lecture Notes in Computer Science\u00a0Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015 (pp. 53\u201361). Springer International Publishing. <\/span><a style=\"letter-spacing: 0.0px;\" href=\"https:\/\/doi.org\/10.1007\/978-3-319-24571-3_7\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1007\/978-3-319-24571-3_7<\/a><\/li><\/ul><h3 id=\"Anew2.5DrepresentationforlymphnodedetectioninCT(CTLymphNodes)-OtherPublicationsUsingThisData\">Other Publications Using This Data<\/h3><p><span>TCIA maintains <a href=\"https:\/\/www.cancerimagingarchive.net\/publications\/\" class=\"external-link\" rel=\"nofollow\">a list of publications<\/a> which leverage our data. <\/span>If you have a publication you'd like to add please<a href=\"http:\/\/www.cancerimagingarchive.net\/support\/\" class=\"external-link\" rel=\"nofollow\"> contact the TCIA Helpdesk<\/a>.<\/p><ul><li>Bier, B., Goldmann, F., Zaech, J. N., Fotouhi, J., Hegeman, R., Grupp, R., . . . Unberath, M. (2019). Learning to detect anatomical landmarks of the pelvis in X-rays from arbitrary views. Int J Comput Assist Radiol Surg. doi: <a href=\"https:\/\/doi.org\/10.1007\/s11548-019-01975-5\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1007\/s11548-019-01975-5<\/a>\u00a0<\/li><li>Esteban, J., Grimm, M., Unberath, M., Zahnd, G., &amp; Navab, N. (2019). Towards Fully Automatic X-Ray to CT Registration. 11769, 631-639. doi: <a href=\"https:\/\/doi.org\/10.1007\/978-3-030-32226-7_70\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1007\/978-3-030-32226-7_70<\/a>\u00a0<\/li><li>Felsner, L., Roser, P., Maier, A., &amp; Riess, C. (2021). Comparison of methods for sensitivity correction in Talbot-Lau computed tomography. Int J Comput Assist Radiol Surg, 16(12), 2099-2106. doi: <a href=\"https:\/\/doi.org\/10.1007\/s11548-021-02487-x\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1007\/s11548-021-02487-x<\/a>\u00a0<\/li><li>Goerres, J., Uneri, A., Jacobson, M., Ramsay, B., De Silva, T., Ketcha, M., . . . Siewerdsen, J. H. (2017). Planning, guidance, and quality assurance of pelvic screw placement using deformable image registration. Phys Med Biol, 62(23), 9018-9038. doi: <a href=\"https:\/\/doi.org\/10.1088\/1361-6560\/aa954f\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1088\/1361-6560\/aa954f<\/a><\/li><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: <a href=\"https:\/\/doi.org\/10.1109\/TMI.2016.2553401\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1109\/TMI.2016.2553401<\/a>\u00a0<\/li><li>ISKENDER, B. (2020). X-ray CT scatter correction by a physics-motivated deep neural network. (M.S. Thesis). University of Illinois at Urbana-Champaign, Retrieved from <a href=\"http:\/\/hdl.handle.net\/2142\/109445\" class=\"external-link\" rel=\"nofollow\">http:\/\/hdl.handle.net\/2142\/109445<\/a><\/li><li>Iuga, A. I., Carolus, H., Hoink, A. J., Brosch, T., Klinder, T., Maintz, D., . . . Pusken, M. (2021). Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networks. BMC Med Imaging, 21(1), 69. doi: <a href=\"https:\/\/doi.org\/10.1186\/s12880-021-00599-z\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1186\/s12880-021-00599-z<\/a>\u00a0<\/li><li>Krishna, P., Robinson, D. L., Bucknill, A., &amp; Lee, P. V. S. (2022). Generation of hemipelvis surface geometry based on statistical shape modelling and contralateral mirroring. Biomechanics and Modeling in Mechanobiology. doi: <a href=\"https:\/\/doi.org\/10.1007\/s10237-022-01594-1\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1007\/s10237-022-01594-1<\/a>\u00a0<\/li><li>Liu, F., Feng, J., Su, W., Lv, Z., Xiao, F., &amp; Qiu, S. (2017). Normalized Euclidean Super-Pixels for Medical Image Segmentation. Paper presented at the International Conference on Intelligent Computing.<\/li><li>Moshfeghifar, F., Gholamalizadeh, T., Ferguson, Z., Schneider, T., Nielsen, M. B., Panozzo, D., . . . Erleben, K. (2022). LibHip: An open-access hip joint model repository suitable for finite element method simulation. Computer Methods and Programs in Biomedicine, 226, 107140. doi: <a href=\"https:\/\/doi.org\/10.1016\/j.cmpb.2022.107140\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1016\/j.cmpb.2022.107140<\/a><\/li><li>Reis, C., Little, B., Lee MacDonald, R., Syme, A., Thomas, C. G., &amp; Robar, J. L. (2021). SBRT of ventricular tachycardia using 4pi optimized trajectories. J Appl Clin Med Phys. doi: <a href=\"https:\/\/doi.org\/10.1002\/acm2.13454\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1002\/acm2.13454<\/a>\u00a0<\/li><li>Roth, H. R., Lu, L., Seff, A., Cherry, K. M., Hoffman, J., Wang, S., . . . Summers, R. M. (2014). A new 2.5 D representation for lymph node detection using random sets of deep convolutional neural network observations. Paper presented at the Med Image Comput Comput Assist Interv. .<\/li><li>Sengupta, D. (2019). Deep Learning Architectures for Automated Image Segmentation. (MS). University of California, Los Angeles, Retrieved from <a href=\"https:\/\/escholarship.org\/uc\/item\/6gb3k51s\" class=\"external-link\" rel=\"nofollow\">https:\/\/escholarship.org\/uc\/item\/6gb3k51s<\/a>\u00a0<\/li><li>Shafiei, A., Bagheri, M., Farhadi, F., Apolo, A. B., Biassou, N. M., Folio, L. R., . . . Summers, R. M. (2021). CT Evaluation of Lymph Nodes That Merge or Split during the Course of a Clinical Trial: Limitations of RECIST 1.1. Radiol Imaging Cancer, 3(3), e200090. doi:<a href=\"https:\/\/doi.org\/10.1148\/rycan.2021200090\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1148\/rycan.2021200090<\/a><\/li><li>Shen, K., Quan, H., Han, J., &amp; Wu, M. (2022). URO-GAN: An untrustworthy region optimization approach for adipose tissue segmentation based on adversarial learning. Applied Intelligence. doi: <a href=\"https:\/\/doi.org\/10.1007\/s10489-021-02976-1\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1007\/s10489-021-02976-1<\/a>\u00a0<\/li><li>Simmons-Ehrhardt, T. (2021). Open osteology: Medical imaging databases as skeletal collections. Forensic Imaging, 26. doi: <a href=\"https:\/\/doi.org\/10.1016\/j.fri.2021.200462\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1016\/j.fri.2021.200462<\/a>\u00a0<\/li><li>Trebeschi, S., Bodalal, Z., van Dijk, N., Boellaard, T. N., Apfaltrer, P., Tareco Bucho, T. M., . . . Beets-Tan, R. G. H. (2021). Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving Immunotherapy. Front Oncol, 11, 637804. doi:10.3389\/fonc.2021.637804<\/li><li>Wang, H., Yi, F., Wang, J., Yi, Z., &amp; Zhang, H. (2022). RECISTSup: Weakly-Supervised Lesion Volume Segmentation Using RECIST Measurement. IEEE Trans Med Imaging, 41(7), 1849-1861. doi:<a href=\"https:\/\/doi.org\/10.1109\/TMI.2022.3149168\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1109\/TMI.2022.3149168<\/a><\/li><li>Wang, Q., Xue, W., Zhang, X., Jin, F., &amp; Hahn, J. (2021). Pixel-wise body composition prediction with a multi-task conditional generative adversarial network. J Biomed Inform, 120, 103866. doi: <a href=\"https:\/\/doi.org\/10.1016\/j.jbi.2021.103866\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1016\/j.jbi.2021.103866<\/a>\u00a0<\/li><li>Wang, Q., Xue, W., Zhang, X., Jin, F., &amp; Hahn, J. (2021). S2FLNet: Hepatic steatosis detection network with body shape. Comput Biol Med, 140, 105088. doi: <a href=\"https:\/\/doi.org\/10.1016\/j.compbiomed.2021.105088\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1016\/j.compbiomed.2021.105088<\/a>\u00a0<\/li><\/ul><\/div><div class=\"tabs-pane \" id=\"1972654624f20fe565d14a77a20bd8c270e8bdbb\" name=\"Versions\" ><h3 id=\"Anew2.5DrepresentationforlymphnodedetectioninCT(CTLymphNodes)-Version5(Current):Updated2023\/03\/31\">Version 5 (Current): Updated 2023\/03\/31<\/h3><div class=\"table-wrap\"><table class=\"wrapped confluenceTable\"><colgroup> <col\/> <col\/> <\/colgroup><tbody><tr><th colspan=\"1\" class=\"confluenceTh\">Data Type<\/th><th colspan=\"1\" class=\"confluenceTh\">Download all or Query\/Filter<\/th><\/tr><tr><td colspan=\"1\" class=\"confluenceTd\">Image, Segmentations (DICOM, 58.4 GB)<\/td><td colspan=\"1\" class=\"confluenceTd\"><div class=\"content-wrapper\"><p>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/19726546\/TCIA_CT_Lymph_Nodes_03-31-2023.tcia?api=v2\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/www.cancerimagingarchive.net\/nbia-search\/?CollectionCriteria=CT%20Lymph%20Nodes\" class=\"external-link\" rel=\"nofollow\"><button class=\"tcia-btn tcia-search-color\"><i class=\"fa fa-search\" \/> Search<\/button><\/a>\u00a0\n<\/span><br\/><\/p><p>(Download requires\u00a0the\u00a0<a href=\"https:\/\/wiki.cancerimagingarchive.net\/display\/NBIA\/Downloading+TCIA+Images\" rel=\"nofollow\">NBIA Data Retriever<\/a>)<\/p><\/div><\/td><\/tr><tr><td colspan=\"1\" class=\"confluenceTd\">Med ABD Lymph Annotations (ZIP)<\/td><td colspan=\"1\" class=\"confluenceTd\"><div class=\"content-wrapper\"><p>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/19726546\/MED_ABD_LYMPH_ANNOTATIONS.zip?version=1&amp;modificationDate=1435166807156&amp;api=v2\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span><\/p><\/div><\/td><\/tr><tr><td colspan=\"1\" class=\"confluenceTd\">Med Lymph Candidate Nodes (ZIP)<\/td><td colspan=\"1\" class=\"confluenceTd\"><div class=\"content-wrapper\"><p>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/19726546\/MED_ABD_LYMPH_CANDIDATES.zip?version=1&amp;modificationDate=1442245247654&amp;api=v2\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span><\/p><\/div><\/td><\/tr><tr><td colspan=\"1\" class=\"confluenceTd\">Med ABD Lymph Masks (ZIP)<\/td><td colspan=\"1\" class=\"confluenceTd\"><div class=\"content-wrapper\"><p>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/19726546\/MED_ABD_LYMPH_MASKS.zip?version=1&amp;modificationDate=1449684916503&amp;api=v2\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span><\/p><\/div><\/td><\/tr><\/tbody><\/table><\/div><p class=\"auto-cursor-target\">Added DICOM version of MED_ABD_LYMPH_MASKS.zip segmentations that were previously available<\/p><h3 id=\"Anew2.5DrepresentationforlymphnodedetectioninCT(CTLymphNodes)-Version4:Updated2015\/12\/14\">Version 4 : Updated 2015\/12\/14<\/h3><div class=\"table-wrap\"><table class=\"wrapped confluenceTable\"><colgroup> <col\/> <col\/> <\/colgroup><tbody><tr><th colspan=\"1\" class=\"confluenceTh\">Data Type<\/th><th colspan=\"1\" class=\"confluenceTh\">Download all or Query\/Filter<\/th><\/tr><tr><td colspan=\"1\" class=\"confluenceTd\">Images (DICOM, 57.8GB)<\/td><td colspan=\"1\" class=\"confluenceTd\"><div class=\"content-wrapper\"><p>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/19726546\/TCIA_CT_Lymph_Nodes_06-22-2015.tcia?version=1&amp;modificationDate=1534787005035&amp;api=v2\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/www.cancerimagingarchive.net\/nbia-search\/?CollectionCriteria=CT%20Lymph%20Nodes\" class=\"external-link\" rel=\"nofollow\"><button class=\"tcia-btn tcia-search-color\"><i class=\"fa fa-search\" \/> Search<\/button><\/a>\u00a0\n<\/span><br\/><\/p><p>(Download requires\u00a0the\u00a0<a href=\"https:\/\/wiki.cancerimagingarchive.net\/display\/NBIA\/Downloading+TCIA+Images\" rel=\"nofollow\">NBIA Data Retriever<\/a>)<\/p><\/div><\/td><\/tr><tr><td colspan=\"1\" class=\"confluenceTd\">Med ABD Lymph Annotations (ZIP)<\/td><td colspan=\"1\" class=\"confluenceTd\"><div class=\"content-wrapper\"><p>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/19726546\/MED_ABD_LYMPH_ANNOTATIONS.zip?version=1&amp;modificationDate=1435166807156&amp;api=v2\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span><\/p><\/div><\/td><\/tr><tr><td colspan=\"1\" class=\"confluenceTd\">Med Lymph Candidate Nodes (ZIP)<\/td><td colspan=\"1\" class=\"confluenceTd\"><div class=\"content-wrapper\"><p>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/19726546\/MED_ABD_LYMPH_CANDIDATES.zip?version=1&amp;modificationDate=1442245247654&amp;api=v2\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span><\/p><\/div><\/td><\/tr><tr><td colspan=\"1\" class=\"confluenceTd\">Med ABD Lymph Masks (ZIP)<\/td><td colspan=\"1\" class=\"confluenceTd\"><div class=\"content-wrapper\"><p>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/19726546\/MED_ABD_LYMPH_MASKS.zip?version=1&amp;modificationDate=1449684916503&amp;api=v2\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span><\/p><\/div><\/td><\/tr><\/tbody><\/table><\/div><p><em> <strong> <a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/19726546\/MED_ABD_LYMPH_MASKS.zip?version=1&amp;modificationDate=1449684916503&amp;api=v2\" rel=\"nofollow\">MED_ABD_LYMPH_MASKS.zip<\/a> <\/strong> <\/em>added via the wiki.<\/p><h3 id=\"Anew2.5DrepresentationforlymphnodedetectioninCT(CTLymphNodes)-Version3:Updated2015\/09\/14\">Version 3: Updated 2015\/09\/14<\/h3><p><a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/19726546\/MED_ABD_LYMPH_CANDIDATES.zip?version=1&amp;modificationDate=1442245247654&amp;api=v2\" rel=\"nofollow\">MED_ABD_LYMPH_CANDIDATES.zip<\/a> <span>\u00a0added via the wiki.<\/span><\/p><h3 id=\"Anew2.5DrepresentationforlymphnodedetectioninCT(CTLymphNodes)-Version2:Updated2015\/06\/24\">Version 2: Updated 2015\/06\/24<\/h3><p><span>\u00a0<\/span> <strong> <em> <a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/19726546\/MED_ABD_LYMPH_ANNOTATIONS.zip?version=1&amp;modificationDate=1435166807156&amp;api=v2\" rel=\"nofollow\">MED_ABD_LYMPH_ANNOTATIONS.zip<\/a> <\/em> <\/strong> <span>\u00a0 added via the wiki.<\/span><\/p><h3 id=\"Anew2.5DrepresentationforlymphnodedetectioninCT(CTLymphNodes)-Version1:Updated2015\/03\/16\">Version 1: Updated 2015\/03\/16<\/h3><p>Image data set uploaded<\/p><p><br\/><\/p><\/div><\/div><\/div><\/div><div><p><em> <strong> <br\/><\/strong> <\/em><\/p><\/div><p><br\/><\/p>","versions":false,"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.\n<ul><li><a href=\"https:\/\/portal.imaging.datacommons.cancer.gov\/explore\/filters\/?collection_id=ct_lymph_nodes\">Imaging Data Commons (IDC)<\/a> (Imaging Data)<\/li><\/ul>","cancer_locations":["Abdomen","Mediastinum"],"collection_page_accessibility":"Public","publications_related":"","version_change_log":"","version_change_log_archived":"","analysis_results":"","collection_status":"Complete","publications_using":"TCIA maintains <a href=\"https:\/\/www.cancerimagingarchive.net\/publications\/\">a list of publications<\/a> which leverage our data. If you have a publication you'd like to add please<a href=\"http:\/\/www.cancerimagingarchive.net\/support\/\"> contact the TCIA Helpdesk<\/a>.\n<ul><li>Bier, B., Goldmann, F., Zaech, J. N., Fotouhi, J., Hegeman, R., Grupp, R., . . . Unberath, M. (2019). Learning to detect anatomical landmarks of the pelvis in X-rays from arbitrary views. Int J Comput Assist Radiol Surg. doi: <a href=\"https:\/\/doi.org\/10.1007\/s11548-019-01975-5\">https:\/\/doi.org\/10.1007\/s11548-019-01975-5<\/a>\u00a0<\/li><li>Esteban, J., Grimm, M., Unberath, M., Zahnd, G., &amp; Navab, N. (2019). Towards Fully Automatic X-Ray to CT Registration. 11769, 631-639. doi: <a href=\"https:\/\/doi.org\/10.1007\/978-3-030-32226-7_70\">https:\/\/doi.org\/10.1007\/978-3-030-32226-7_70<\/a>\u00a0<\/li><li>Felsner, L., Roser, P., Maier, A., &amp; Riess, C. (2021). Comparison of methods for sensitivity correction in Talbot-Lau computed tomography. Int J Comput Assist Radiol Surg, 16(12), 2099-2106. doi: <a href=\"https:\/\/doi.org\/10.1007\/s11548-021-02487-x\">https:\/\/doi.org\/10.1007\/s11548-021-02487-x<\/a>\u00a0<\/li><li>Goerres, J., Uneri, A., Jacobson, M., Ramsay, B., De Silva, T., Ketcha, M., . . . Siewerdsen, J. H. (2017). Planning, guidance, and quality assurance of pelvic screw placement using deformable image registration. Phys Med Biol, 62(23), 9018-9038. doi: <a href=\"https:\/\/doi.org\/10.1088\/1361-6560\/aa954f\">https:\/\/doi.org\/10.1088\/1361-6560\/aa954f<\/a><\/li><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: <a href=\"https:\/\/doi.org\/10.1109\/TMI.2016.2553401\">https:\/\/doi.org\/10.1109\/TMI.2016.2553401<\/a>\u00a0<\/li><li>ISKENDER, B. (2020). X-ray CT scatter correction by a physics-motivated deep neural network. (M.S. Thesis). University of Illinois at Urbana-Champaign, Retrieved from <a href=\"http:\/\/hdl.handle.net\/2142\/109445\">http:\/\/hdl.handle.net\/2142\/109445<\/a><\/li><li>Iuga, A. I., Carolus, H., Hoink, A. J., Brosch, T., Klinder, T., Maintz, D., . . . Pusken, M. (2021). Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networks. BMC Med Imaging, 21(1), 69. doi: <a href=\"https:\/\/doi.org\/10.1186\/s12880-021-00599-z\">https:\/\/doi.org\/10.1186\/s12880-021-00599-z<\/a>\u00a0<\/li><li>Krishna, P., Robinson, D. L., Bucknill, A., &amp; Lee, P. V. S. (2022). Generation of hemipelvis surface geometry based on statistical shape modelling and contralateral mirroring. Biomechanics and Modeling in Mechanobiology. doi: <a href=\"https:\/\/doi.org\/10.1007\/s10237-022-01594-1\">https:\/\/doi.org\/10.1007\/s10237-022-01594-1<\/a>\u00a0<\/li><li>Liu, F., Feng, J., Su, W., Lv, Z., Xiao, F., &amp; Qiu, S. (2017). Normalized Euclidean Super-Pixels for Medical Image Segmentation. Paper presented at the International Conference on Intelligent Computing.<\/li><li>Moshfeghifar, F., Gholamalizadeh, T., Ferguson, Z., Schneider, T., Nielsen, M. B., Panozzo, D., . . . Erleben, K. (2022). LibHip: An open-access hip joint model repository suitable for finite element method simulation. Computer Methods and Programs in Biomedicine, 226, 107140. doi: <a href=\"https:\/\/doi.org\/10.1016\/j.cmpb.2022.107140\">https:\/\/doi.org\/10.1016\/j.cmpb.2022.107140<\/a><\/li><li>Reis, C., Little, B., Lee MacDonald, R., Syme, A., Thomas, C. G., &amp; Robar, J. L. (2021). SBRT of ventricular tachycardia using 4pi optimized trajectories. J Appl Clin Med Phys. doi: <a href=\"https:\/\/doi.org\/10.1002\/acm2.13454\">https:\/\/doi.org\/10.1002\/acm2.13454<\/a>\u00a0<\/li><li>Roth, H. R., Lu, L., Seff, A., Cherry, K. M., Hoffman, J., Wang, S., . . . Summers, R. M. (2014). A new 2.5 D representation for lymph node detection using random sets of deep convolutional neural network observations. Paper presented at the Med Image Comput Comput Assist Interv. .<\/li><li>Sengupta, D. (2019). Deep Learning Architectures for Automated Image Segmentation. (MS). University of California, Los Angeles, Retrieved from <a href=\"https:\/\/escholarship.org\/uc\/item\/6gb3k51s\">https:\/\/escholarship.org\/uc\/item\/6gb3k51s<\/a>\u00a0<\/li><li>Shafiei, A., Bagheri, M., Farhadi, F., Apolo, A. B., Biassou, N. M., Folio, L. R., . . . Summers, R. M. (2021). CT Evaluation of Lymph Nodes That Merge or Split during the Course of a Clinical Trial: Limitations of RECIST 1.1. Radiol Imaging Cancer, 3(3), e200090. doi:<a href=\"https:\/\/doi.org\/10.1148\/rycan.2021200090\">https:\/\/doi.org\/10.1148\/rycan.2021200090<\/a><\/li><li>Shen, K., Quan, H., Han, J., &amp; Wu, M. (2022). URO-GAN: An untrustworthy region optimization approach for adipose tissue segmentation based on adversarial learning. Applied Intelligence. doi: <a href=\"https:\/\/doi.org\/10.1007\/s10489-021-02976-1\">https:\/\/doi.org\/10.1007\/s10489-021-02976-1<\/a>\u00a0<\/li><li>Simmons-Ehrhardt, T. (2021). Open osteology: Medical imaging databases as skeletal collections. Forensic Imaging, 26. doi: <a href=\"https:\/\/doi.org\/10.1016\/j.fri.2021.200462\">https:\/\/doi.org\/10.1016\/j.fri.2021.200462<\/a>\u00a0<\/li><li>Trebeschi, S., Bodalal, Z., van Dijk, N., Boellaard, T. N., Apfaltrer, P., Tareco Bucho, T. M., . . . Beets-Tan, R. G. H. (2021). Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving Immunotherapy. Front Oncol, 11, 637804. doi:10.3389\/fonc.2021.637804<\/li><li>Wang, H., Yi, F., Wang, J., Yi, Z., &amp; Zhang, H. (2022). RECISTSup: Weakly-Supervised Lesion Volume Segmentation Using RECIST Measurement. IEEE Trans Med Imaging, 41(7), 1849-1861. doi:<a href=\"https:\/\/doi.org\/10.1109\/TMI.2022.3149168\">https:\/\/doi.org\/10.1109\/TMI.2022.3149168<\/a><\/li><li>Wang, Q., Xue, W., Zhang, X., Jin, F., &amp; Hahn, J. (2021). Pixel-wise body composition prediction with a multi-task conditional generative adversarial network. J Biomed Inform, 120, 103866. doi: <a href=\"https:\/\/doi.org\/10.1016\/j.jbi.2021.103866\">https:\/\/doi.org\/10.1016\/j.jbi.2021.103866<\/a>\u00a0<\/li><li>Wang, Q., Xue, W., Zhang, X., Jin, F., &amp; Hahn, J. (2021). S2FLNet: Hepatic steatosis detection network with body shape. Comput Biol Med, 140, 105088. doi: <a href=\"https:\/\/doi.org\/10.1016\/j.compbiomed.2021.105088\">https:\/\/doi.org\/10.1016\/j.compbiomed.2021.105088<\/a>\u00a0<\/li><\/ul>","species":["Human"],"collection_title":"A new 2.5 D representation for lymph node detection in CT","detailed_description":"The DICOM files were created from volumetric images (Analyze and NifTI) using this from ITK: \u00a0 <strong> <em> <a href=\"http:\/\/www.itk.org\/Doxygen\/html\/Examples_2IO_2ImageReadDicomSeriesWrite_8cxx-example.html\">http:\/\/www.itk.org\/Doxygen\/html\/Examples_2IO_2ImageReadDicomSeriesWrite_8cxx-example.html<\/a>.<\/em> <\/strong>\n<h3>Annotation files<\/h3>\n\u00a0<strong> <em> <a download=\"\" href=\"\/wp-content\/uploads\/MED_ABD_LYMPH_ANNOTATIONS.zip\" target=\"_blank\">MED_ABD_LYMPH_ANNOTATIONS.zip<\/a> <\/em> <\/strong>\u00a0(new 6\/24\/2015). The annotations include a folder for each case with text files of voxel indices, physical coordinates, size measurements and a\u00a0<a href=\"http:\/\/mitk.org\/\">MITK\u00a0<\/a>point set file (.mps), which can be visualized using the\u00a0<a href=\"http:\/\/mitk.org\/Download\">MITK workbench<\/a>\u00a0(Note: only release\u00a0<a href=\"http:\/\/mitk.org\/wiki\/MITK_ReleaseNotes_2014.10\" title=\"MITK ReleaseNotes 2014.10\">2014.10.0<\/a>\u00a0and later supports visualization of point set files using the \"point set interaction plugin\"). Abdominal size measurements\u00a0include the longest and shortest axis in axial view of a lymph node. The shortest axis is used for the RECIST criteria. The mediastinal set only includes the shortest axis.\n<h3>Mediastinal and abdominal lymph nodes<\/h3>\nComputer-generated candidate detections for mediastinal and abdominal lymph nodes (produced by methods in [K. Cherry et al., SPIE Med. Img. 2014] and [J. Liu et al., SPIE Med. Img. 2014]]). \u00a0See attached:\u00a0<a download=\"\" href=\"\/wp-content\/uploads\/MED_ABD_LYMPH_CANDIDATES.zip\" target=\"_blank\">MED_ABD_LYMPH_CANDIDATES.zip<\/a>\u00a0(new 9\/14\/2015).\n<em> <strong> <a download=\"\" href=\"\/wp-content\/uploads\/MED_ABD_LYMPH_MASKS.zip\" target=\"_blank\">MED_ABD_LYMPH_MASKS.zip<\/a>\u00a0<\/strong> <\/em>(new 12\/8\/2015): These files contain a compressed NifTI image (*.nii.gz) for each patient with manually traced lymph node segmentations. Note: these segmentation masks were produced independently to the centroid annotations in MED_ABD_LYMPH_ANNOTATIONS.zip. There is an overlapping set of lymph nodes marked in both files but the indexing does not align.\u00a0 On 3\/31\/2023 (version 5) a DICOM-SEG version of these data were added to the collection.\nPlease cite the following paper when using the segmentation masks:\nA Seff, L Lu, A Barbu, H Roth, HC Shin, RM Summers. <strong>Leveraging Mid-Level Semantic Boundary Cues for Automated Lymph Node Detection<\/strong>. Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015, 53-61 (<a href=\"http:\/\/link.springer.com\/chapter\/10.1007\/978-3-319-24571-3_7\">http:\/\/link.springer.com\/chapter\/10.1007\/978-3-319-24571-3_7<\/a>)","related_analysis_results":false,"subjects":"176","collection_short_title":"CT Lymph Nodes","data_types":["CT","SEG"],"date_updated":"2023-09-13","collection_browse_title":"","supporting_data":["Image Analyses"],"collection_featured_image":false,"collection_summary":"This collection consists of Computed Tomography (CT) images of the mediastinum and abdomen in which lymph node positions are marked by radiologists at the National Institutes of Health, Clinical Center. Radiologists at the <em> <a href=\"https:\/\/clinicalcenter.nih.gov\/meet-our-doctors\/rsummers.html\"> <strong>I<\/strong><strong>maging Biomarkers and Computer-Aided Diagnosis Laboratory<\/strong> <\/a> <\/em> labeled a total of 388 mediastinal lymph nodes in CT images of 90 patients and a total of 595 abdominal lymph nodes in 86 patients.\nThe collection is aimed at the medical image computing community for developing and assessing computer-aided detection methods. Automated detection of lymph nodes can be an important clinical diagnostic tool but is very challenging due to the low contrast of surrounding structures in CT and to their varying sizes, poses, shapes and sparsely distributed locations. This data set is made available to make direct comparison to other detection methods in order to advance the state of the art.","collection_acknowledgements":"<ul><li>We would like to acknowledge the individuals and institutions that have provided data for this collection: National Institutes of Health, Bethesda MD. \u00a0 Special thanks to  <strong>Dr. Holger R. Roth <\/strong>and<strong> Dr. Ronald Summers, <em> <strong> <a href=\"https:\/\/clinicalcenter.nih.gov\/meet-our-doctors\/rsummers.html\">Imaging Biomarkers and Computer-Aided Diagnosis Laboratory<\/a> <\/strong> <\/em> ,  <\/strong> Grant Magnuson Clinical Center .<\/li><li>Conversion of the segmentations into DICOM SEG representation was completed by Cosmin Ciausu using dcmqi (<a href=\"https:\/\/gcc02.safelinks.protection.outlook.com\/?url=https%3A%2F%2Fgithub.com%2FQIICR%2Fdcmqi&amp;data=05%7C01%7Ckirbyju%40mail.nih.gov%7Cf91c2d574a344e8d972d08db3217a378%7C14b77578977342d58507251ca2dc2b06%7C0%7C0%7C638158849339372035%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&amp;sdata=tHPQabx6hJ0Nry7BEjckaZMsuQ3H4%2FhNT5ijscuINcs%3D&amp;reserved=0\" title=\"Original URL: https:\/\/github.com\/QIICR\/dcmqi. Click or tap if you trust this link.\">https:\/\/github.com\/QIICR\/dcmqi<\/a>), assisted by Andrey Fedorov, David Clunie, and other members of the <a href=\"https:\/\/portal.imaging.datacommons.cancer.gov\/\">NCI Imaging Data Commons<\/a>\u00a0team.\u00a0NCI Imaging Data Commons consortium is supported by the contract number 19X037Q from Leidos Biomedical Research under Task Order HHSN26100071 under Contract Number HHSN261201500003l from NCI.<\/li><\/ul>\n<br\/>","collection_funding":"","hide_from_browse_table":[],"_links":{"self":[{"href":"https:\/\/cm.vastapps.dev\/api\/v1\/collections\/5530"}],"collection":[{"href":"https:\/\/cm.vastapps.dev\/api\/v1\/collections"}],"about":[{"href":"https:\/\/cm.vastapps.dev\/api\/wp\/v2\/types\/tcia_collection"}],"wp:attachment":[{"href":"https:\/\/cm.vastapps.dev\/api\/wp\/v2\/media?parent=5530"}],"wp:term":[{"taxonomy":"tcia_citation_tax","embeddable":true,"href":"https:\/\/cm.vastapps.dev\/api\/v1\/citation-tax?post=5530"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}