{"id":779606,"date":"2026-09-23T04:32:25","date_gmt":"2026-09-23T04:32:25","guid":{"rendered":"https:\/\/www.newsbeep.com\/uk\/779606\/"},"modified":"2026-09-23T04:32:25","modified_gmt":"2026-09-23T04:32:25","slug":"rapid-patient-specific-neural-networks-for-x-ray-to-volume-registration","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/uk\/779606\/","title":{"rendered":"Rapid patient-specific neural networks for X-ray to volume registration"},"content":{"rendered":"<p class=\"c-article-references__text\" id=\"ref-CR1\">Unberath, M. et al. The impact of machine learning on 2D\/3D registration for image-guided interventions: a systematic review and perspective. Front. Robot. AI 8, 716007 (2021).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.3389\/frobt.2021.716007\" data-track-item_id=\"10.3389\/frobt.2021.716007\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.3389%2Ffrobt.2021.716007\" aria-label=\"Article reference 1\" data-doi=\"10.3389\/frobt.2021.716007\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=34527706\" aria-label=\"PubMed reference 1\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC8436154\" aria-label=\"PubMed Central reference 1\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 1\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=The%20impact%20of%20machine%20learning%20on%202D%2F3D%20registration%20for%20image-guided%20interventions%3A%20a%20systematic%20review%20and%20perspective&amp;journal=Front.%20Robot.%20AI&amp;doi=10.3389%2Ffrobt.2021.716007&amp;volume=8&amp;publication_year=2021&amp;author=Unberath%2CM\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR2\">Yip, M. et al. Artificial intelligence meets medical robotics. Science 381, 141\u2013146 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1126\/science.adj3312\" data-track-item_id=\"10.1126\/science.adj3312\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1126%2Fscience.adj3312\" aria-label=\"Article reference 2\" data-doi=\"10.1126\/science.adj3312\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2023Sci...381..141Y\" aria-label=\"ADS reference 2\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3sXhsVKht7jN\" aria-label=\"CAS reference 2\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=37440630\" aria-label=\"PubMed reference 2\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 2\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Artificial%20intelligence%20meets%20medical%20robotics&amp;journal=Science&amp;doi=10.1126%2Fscience.adj3312&amp;volume=381&amp;pages=141-146&amp;publication_year=2023&amp;author=Yip%2CM\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR3\">Penney, G. P. et al. A comparison of similarity measures for use in 2D\/3D medical image registration. IEEE Trans. Med. Imaging 17, 586\u2013595 (1998).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1109\/42.730403\" data-track-item_id=\"10.1109\/42.730403\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1109%2F42.730403\" aria-label=\"Article reference 3\" data-doi=\"10.1109\/42.730403\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=1998ITMI...17..586P\" aria-label=\"ADS reference 3\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:STN:280:DyaK1M%2FlvFCntQ%3D%3D\" aria-label=\"CAS reference 3\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=9845314\" aria-label=\"PubMed reference 3\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 3\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=A%20comparison%20of%20similarity%20measures%20for%20use%20in%202D%2F3D%20medical%20image%20registration&amp;journal=IEEE%20Trans.%20Med.%20Imaging&amp;doi=10.1109%2F42.730403&amp;volume=17&amp;pages=586-595&amp;publication_year=1998&amp;author=Penney%2CGP\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR4\">Knaan, D. &amp; Joskowicz, L. Effective intensity-based 2D\/3D rigid registration between fluoroscopic X-ray and CT. In Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention (eds Ellis, R. E. &amp; Peters, T. M.) 351\u2013358 (Springer, 2003).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR5\">Grupp, R. B. et al. Automatic annotation of hip anatomy in fluoroscopy for robust and efficient 2D\/3D registration. Int. J. Comput. Assist. Radiol. Surg. 15, 759\u2013769 (2020).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"noopener nofollow\" data-track-label=\"10.1007\/s11548-020-02162-7\" data-track-item_id=\"10.1007\/s11548-020-02162-7\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/link.springer.com\/doi\/10.1007\/s11548-020-02162-7\" aria-label=\"Article reference 5\" data-doi=\"10.1007\/s11548-020-02162-7\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=32333361\" aria-label=\"PubMed reference 5\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC7263976\" aria-label=\"PubMed Central reference 5\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 5\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Automatic%20annotation%20of%20hip%20anatomy%20in%20fluoroscopy%20for%20robust%20and%20efficient%202D%2F3D%20registration&amp;journal=Int.%20J.%20Comput.%20Assist.%20Radiol.%20Surg.&amp;doi=10.1007%2Fs11548-020-02162-7&amp;volume=15&amp;pages=759-769&amp;publication_year=2020&amp;author=Grupp%2CRB\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR6\">Grimm, M., Esteban, J., Unberath, M. &amp; Navab, N. Pose-dependent weights and domain randomization for fully automatic X-ray to CT registration. IEEE Trans. Med. Imaging 40, 2221\u20132232 (2021).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1109\/TMI.2021.3073815\" data-track-item_id=\"10.1109\/TMI.2021.3073815\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1109%2FTMI.2021.3073815\" aria-label=\"Article reference 6\" data-doi=\"10.1109\/TMI.2021.3073815\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2021ITMI...40.2221G\" aria-label=\"ADS reference 6\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=33861701\" aria-label=\"PubMed reference 6\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 6\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Pose-dependent%20weights%20and%20domain%20randomization%20for%20fully%20automatic%20X-ray%20to%20CT%20registration&amp;journal=IEEE%20Trans.%20Med.%20Imaging&amp;doi=10.1109%2FTMI.2021.3073815&amp;volume=40&amp;pages=2221-2232&amp;publication_year=2021&amp;author=Grimm%2CM&amp;author=Esteban%2CJ&amp;author=Unberath%2CM&amp;author=Navab%2CN\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR7\">Mahesh, M., Ansari, A. J. &amp; Mettler, F. A. Jr Patient exposure from radiologic and nuclear medicine procedures in the United States and worldwide: 2009\u20132018. Radiology 307, e221263 (2022).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1148\/radiol.221263\" data-track-item_id=\"10.1148\/radiol.221263\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1148%2Fradiol.221263\" aria-label=\"Article reference 7\" data-doi=\"10.1148\/radiol.221263\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=36511806\" aria-label=\"PubMed reference 7\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC10050133\" aria-label=\"PubMed Central reference 7\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 7\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Patient%20exposure%20from%20radiologic%20and%20nuclear%20medicine%20procedures%20in%20the%20United%20States%20and%20worldwide%3A%202009%E2%80%932018&amp;journal=Radiology&amp;doi=10.1148%2Fradiol.221263&amp;volume=307&amp;publication_year=2022&amp;author=Mahesh%2CM&amp;author=Ansari%2CAJ&amp;author=Mettler%2CFA\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR8\">Cornelis, F. H., Dzaye, O., Schoellnast, H. &amp; Solomon, S. B. Imaging of interventional therapies in oncology: image guidance, robotics, and fusion systems. In Interventional Oncology (eds Fong, Y. et al.) 1\u201317 <a href=\"https:\/\/doi.org\/10.1007\/978-3-030-51192-0_19-1\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"10.1007\/978-3-030-51192-0_19-1\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/doi.org\/10.1007\/978-3-030-51192-0_19-1<\/a> (Springer, 2023).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR9\">Jhawar, B. S., Mitsis, D. &amp; Duggal, N. Wrong-sided and wrong-level neurosurgery: a national survey. J. Neurosurg. Spine 7, 467\u2013472 (2007).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.3171\/SPI-07\/11\/467\" data-track-item_id=\"10.3171\/SPI-07\/11\/467\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.3171%2FSPI-07%2F11%2F467\" aria-label=\"Article reference 9\" data-doi=\"10.3171\/SPI-07\/11\/467\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=17977186\" aria-label=\"PubMed reference 9\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 9\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Wrong-sided%20and%20wrong-level%20neurosurgery%3A%20a%20national%20survey&amp;journal=J.%20Neurosurg.%20Spine&amp;doi=10.3171%2FSPI-07%2F11%2F467&amp;volume=7&amp;pages=467-472&amp;publication_year=2007&amp;author=Jhawar%2CBS&amp;author=Mitsis%2CD&amp;author=Duggal%2CN\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR10\">Tonetti, J., Boudissa, M., Kerschbaumer, G. &amp; Seurat, O. Role of 3D intraoperative imaging in orthopedic and trauma surgery. Orthop. Traumatol. Surg. Res. 106, S19\u2013S25 (2020).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1016\/j.otsr.2019.05.021\" data-track-item_id=\"10.1016\/j.otsr.2019.05.021\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1016%2Fj.otsr.2019.05.021\" aria-label=\"Article reference 10\" data-doi=\"10.1016\/j.otsr.2019.05.021\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=31734181\" aria-label=\"PubMed reference 10\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 10\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Role%20of%203D%20intraoperative%20imaging%20in%20orthopedic%20and%20trauma%20surgery&amp;journal=Orthop.%20Traumatol.%20Surg.%20Res.&amp;doi=10.1016%2Fj.otsr.2019.05.021&amp;volume=106&amp;pages=S19-S25&amp;publication_year=2020&amp;author=Tonetti%2CJ&amp;author=Boudissa%2CM&amp;author=Kerschbaumer%2CG&amp;author=Seurat%2CO\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR11\">Abumoussa, A. et al. Machine learning for automated and real-time two-dimensional to three-dimensional registration of the spine using a single radiograph. Neurosurg. Focus 54, E16 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.3171\/2023.3.FOCUS2345\" data-track-item_id=\"10.3171\/2023.3.FOCUS2345\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.3171%2F2023.3.FOCUS2345\" aria-label=\"Article reference 11\" data-doi=\"10.3171\/2023.3.FOCUS2345\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=37552666\" aria-label=\"PubMed reference 11\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 11\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Machine%20learning%20for%20automated%20and%20real-time%20two-dimensional%20to%20three-dimensional%20registration%20of%20the%20spine%20using%20a%20single%20radiograph&amp;journal=Neurosurg.%20Focus&amp;doi=10.3171%2F2023.3.FOCUS2345&amp;volume=54&amp;publication_year=2023&amp;author=Abumoussa%2CA\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR12\">Naik, R. R., Hoblidar, A., Bhat, S. N., Ampar, N. &amp; Kundangar, R. A hybrid 3D-2D image registration framework for pedicle screw trajectory registration between intraoperative X-ray image and preoperative CT image. J. Imaging 8, 185 (2022).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.3390\/jimaging8070185\" data-track-item_id=\"10.3390\/jimaging8070185\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.3390%2Fjimaging8070185\" aria-label=\"Article reference 12\" data-doi=\"10.3390\/jimaging8070185\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=35877629\" aria-label=\"PubMed reference 12\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC9324544\" aria-label=\"PubMed Central reference 12\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 12\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=A%20hybrid%203D-2D%20image%20registration%20framework%20for%20pedicle%20screw%20trajectory%20registration%20between%20intraoperative%20X-ray%20image%20and%20preoperative%20CT%20image&amp;journal=J.%20Imaging&amp;doi=10.3390%2Fjimaging8070185&amp;volume=8&amp;publication_year=2022&amp;author=Naik%2CRR&amp;author=Hoblidar%2CA&amp;author=Bhat%2CSN&amp;author=Ampar%2CN&amp;author=Kundangar%2CR\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR13\">Metz, C. T. et al. Patient specific 4D coronary models from ECG-gated CTA data for intra-operative dynamic alignment of CTA with X-ray images. In Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention (eds Guang-Zhong, Y. et al.) 369\u2013376 (Springer, 2009).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR14\">Wagner, M., Schafer, S., Strother, C. &amp; Mistretta, C. 4D interventional device reconstruction from biplane fluoroscopy. Med. Phys. 43, 1324\u20131334 (2016).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1118\/1.4941950\" data-track-item_id=\"10.1118\/1.4941950\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1118%2F1.4941950\" aria-label=\"Article reference 14\" data-doi=\"10.1118\/1.4941950\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=26936717\" aria-label=\"PubMed reference 14\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC4760973\" aria-label=\"PubMed Central reference 14\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 14\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=4D%20interventional%20device%20reconstruction%20from%20biplane%20fluoroscopy&amp;journal=Med.%20Phys.&amp;doi=10.1118%2F1.4941950&amp;volume=43&amp;pages=1324-1334&amp;publication_year=2016&amp;author=Wagner%2CM&amp;author=Schafer%2CS&amp;author=Strother%2CC&amp;author=Mistretta%2CC\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR15\">Huynh, E. et al. Artificial intelligence in radiation oncology. Nat. Rev. Clin. Oncol. 17, 771\u2013781 (2020).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41571-020-0417-8\" data-track-item_id=\"10.1038\/s41571-020-0417-8\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41571-020-0417-8\" aria-label=\"Article reference 15\" data-doi=\"10.1038\/s41571-020-0417-8\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=32843739\" aria-label=\"PubMed reference 15\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 15\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Artificial%20intelligence%20in%20radiation%20oncology&amp;journal=Nat.%20Rev.%20Clin.%20Oncol.&amp;doi=10.1038%2Fs41571-020-0417-8&amp;volume=17&amp;pages=771-781&amp;publication_year=2020&amp;author=Huynh%2CE\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR16\">Kim, Y. et al. Telerobotic neurovascular interventions with magnetic manipulation. Sci. Robot. 7, eabg9907 (2022).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1126\/scirobotics.abg9907\" data-track-item_id=\"10.1126\/scirobotics.abg9907\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1126%2Fscirobotics.abg9907\" aria-label=\"Article reference 16\" data-doi=\"10.1126\/scirobotics.abg9907\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=35417201\" aria-label=\"PubMed reference 16\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC9254892\" aria-label=\"PubMed Central reference 16\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 16\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Telerobotic%20neurovascular%20interventions%20with%20magnetic%20manipulation&amp;journal=Sci.%20Robot.&amp;doi=10.1126%2Fscirobotics.abg9907&amp;volume=7&amp;publication_year=2022&amp;author=Kim%2CY\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR17\">Gu, W., Gao, C., Grupp, R., Fotouhi, J. &amp; Unberath, M. Extended capture range of rigid 2D\/3D registration by estimating Riemannian pose gradients. In Proc. International Workshop on Machine Learning in Medical Imaging (eds Liu, M. et al.) 281\u2013291 (Springer, 2020).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR18\">Gopalakrishnan, V. &amp; Golland, P. Fast auto-differentiable digitally reconstructed radiographs for solving inverse problems in intraoperative imaging. In Proc. Workshop on Clinical Image-Based Procedures (eds Chen, Y. et al.) 1\u201311 (Springer, 2022).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR19\">Gao, C. et al. A fully differentiable framework for 2D\/3D registration and the projective spatial transformers. IEEE Trans. Med. Imaging 43, 275\u2013285 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1109\/TMI.2023.3299588\" data-track-item_id=\"10.1109\/TMI.2023.3299588\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1109%2FTMI.2023.3299588\" aria-label=\"Article reference 19\" data-doi=\"10.1109\/TMI.2023.3299588\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2024ITMI...43..275G\" aria-label=\"ADS reference 19\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 19\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=A%20fully%20differentiable%20framework%20for%202D%2F3D%20registration%20and%20the%20projective%20spatial%20transformers&amp;journal=IEEE%20Trans.%20Med.%20Imaging&amp;doi=10.1109%2FTMI.2023.3299588&amp;volume=43&amp;pages=275-285&amp;publication_year=2023&amp;author=Gao%2CC\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR20\">Grupp, R. B. et al. Pose estimation of periacetabular osteotomy fragments with intraoperative X-ray navigation. IEEE Trans. Biomed. Eng. 67, 441\u2013452 (2019).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1109\/TBME.2019.2915165\" data-track-item_id=\"10.1109\/TBME.2019.2915165\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1109%2FTBME.2019.2915165\" aria-label=\"Article reference 20\" data-doi=\"10.1109\/TBME.2019.2915165\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=31059424\" aria-label=\"PubMed reference 20\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC7297497\" aria-label=\"PubMed Central reference 20\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 20\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Pose%20estimation%20of%20periacetabular%20osteotomy%20fragments%20with%20intraoperative%20X-ray%20navigation&amp;journal=IEEE%20Trans.%20Biomed.%20Eng.&amp;doi=10.1109%2FTBME.2019.2915165&amp;volume=67&amp;pages=441-452&amp;publication_year=2019&amp;author=Grupp%2CRB\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR21\">Bier, B. et al. Learning to detect anatomical landmarks of the pelvis in X-rays from arbitrary views. Int. J. Comput. Assist. Radiol. Surg. 14, 1463\u20131473 (2019).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"noopener nofollow\" data-track-label=\"10.1007\/s11548-019-01975-5\" data-track-item_id=\"10.1007\/s11548-019-01975-5\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/link.springer.com\/doi\/10.1007\/s11548-019-01975-5\" aria-label=\"Article reference 21\" data-doi=\"10.1007\/s11548-019-01975-5\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=31006106\" aria-label=\"PubMed reference 21\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC7297500\" aria-label=\"PubMed Central reference 21\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 21\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Learning%20to%20detect%20anatomical%20landmarks%20of%20the%20pelvis%20in%20X-rays%20from%20arbitrary%20views&amp;journal=Int.%20J.%20Comput.%20Assist.%20Radiol.%20Surg.&amp;doi=10.1007%2Fs11548-019-01975-5&amp;volume=14&amp;pages=1463-1473&amp;publication_year=2019&amp;author=Bier%2CB\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR22\">Shrestha, P., Xie, C., Yoshii, Y. &amp; Kitahara, I. Rayemb: arbitrary landmark detection in X-ray images using ray embedding subspace. In Proc. Asian Conference on Computer Vision (eds Cho, M. et al.) 665\u2013681 (Springer, 2024).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR23\">Miao, S., Wang, Z. J. &amp; Liao, R. A CNN regression approach for real-time 2D\/3D registration. IEEE Trans. Med. Imaging 35, 1352\u20131363 (2016).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1109\/TMI.2016.2521800\" data-track-item_id=\"10.1109\/TMI.2016.2521800\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1109%2FTMI.2016.2521800\" aria-label=\"Article reference 23\" data-doi=\"10.1109\/TMI.2016.2521800\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2016ITMI...35.1352M\" aria-label=\"ADS reference 23\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 23\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=A%20CNN%20regression%20approach%20for%20real-time%202D%2F3D%20registration&amp;journal=IEEE%20Trans.%20Med.%20Imaging&amp;doi=10.1109%2FTMI.2016.2521800&amp;volume=35&amp;pages=1352-1363&amp;publication_year=2016&amp;author=Miao%2CS&amp;author=Wang%2CZJ&amp;author=Liao%2CR\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR24\">Bui, M., Albarqouni, S., Schrapp, M., Navab, N. &amp; Ilic, S. X-ray posenet: 6 DoF pose estimation for mobile X-ray devices. In Proc. 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) 1036\u20131044 (IEEE, 2017).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR25\">Zhang, B. et al. A patient-specific self-supervised model for automatic X-ray\/CT registration. In Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention (eds Greenspan, H. et al.) 515\u2013524 (Springer, 2023).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR26\">Gopalakrishnan, V., Dey, N. &amp; Golland, P. Intraoperative 2D\/3D image registration via differentiable X-ray rendering. In Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition 11662\u201311672 (IEEE, 2024).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR27\">Wasserthal, J. et al. TotalSegmentator: robust segmentation of 104 anatomic structures in CT images. Radiol. Artif. Intell. 5, e230024 (2023).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1148\/ryai.230024\" data-track-item_id=\"10.1148\/ryai.230024\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1148%2Fryai.230024\" aria-label=\"Article reference 27\" data-doi=\"10.1148\/ryai.230024\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=37795137\" aria-label=\"PubMed reference 27\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed central reference\" data-track-action=\"pubmed central reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC10546353\" aria-label=\"PubMed Central reference 27\" target=\"_blank\">PubMed Central<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 27\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=TotalSegmentator%3A%20robust%20segmentation%20of%20104%20anatomic%20structures%20in%20CT%20images&amp;journal=Radiol.%20Artif.%20Intell.&amp;doi=10.1148%2Fryai.230024&amp;volume=5&amp;publication_year=2023&amp;author=Wasserthal%2CJ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR28\">Jaus, A. et al. Towards unifying anatomy segmentation: automated generation of a full-body CT dataset. In Proc. 2024 IEEE International Conference on Image Processing (ICIP) 41\u201347 (IEEE, 2024).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR29\">Magnetic Resonance Angiography Atlas Dataset. NeuroImaging Tools &amp; Resources Collaboratory <a href=\"https:\/\/www.nitrc.org\/projects\/icbmmra\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/www.nitrc.org\/projects\/icbmmra\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/www.nitrc.org\/projects\/icbmmra<\/a> (2017).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR30\">Liu, P. et al. Deep learning to segment pelvic bones: large-scale CT datasets and baseline models. Int. J. Comput. Assist. Radiol. Surg. 16, 749\u2013756 (2021).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"noopener nofollow\" data-track-label=\"10.1007\/s11548-021-02363-8\" data-track-item_id=\"10.1007\/s11548-021-02363-8\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/link.springer.com\/doi\/10.1007\/s11548-021-02363-8\" aria-label=\"Article reference 30\" data-doi=\"10.1007\/s11548-021-02363-8\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=33864189\" aria-label=\"PubMed reference 30\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 30\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Deep%20learning%20to%20segment%20pelvic%20bones%3A%20large-scale%20CT%20datasets%20and%20baseline%20models&amp;journal=Int.%20J.%20Comput.%20Assist.%20Radiol.%20Surg.&amp;doi=10.1007%2Fs11548-021-02363-8&amp;volume=16&amp;pages=749-756&amp;publication_year=2021&amp;author=Liu%2CP\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR31\">Yushkevich, P. A. et al. Fast automatic segmentation of hippocampal subfields and medial temporal lobe subregions in 3 Tesla and 7 Tesla T2-weighted MRI. Alzheimers Dement. 12, P126\u2013P127 (2016).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1016\/j.jalz.2016.06.205\" data-track-item_id=\"10.1016\/j.jalz.2016.06.205\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1016%2Fj.jalz.2016.06.205\" aria-label=\"Article reference 31\" data-doi=\"10.1016\/j.jalz.2016.06.205\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 31\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Fast%20automatic%20segmentation%20of%20hippocampal%20subfields%20and%20medial%20temporal%20lobe%20subregions%20in%203%20Tesla%20and%207%20Tesla%20T2-weighted%20MRI&amp;journal=Alzheimers%20Dement.&amp;doi=10.1016%2Fj.jalz.2016.06.205&amp;volume=12&amp;pages=P126-P127&amp;publication_year=2016&amp;author=Yushkevich%2CPA\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR32\">Zhou, Y., Barnes, C., Lu, J., Yang, J. &amp; Li, H. On the continuity of rotation representations in neural networks. In Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition 5745\u20135753 (IEEE, 2019).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR33\">Geist, A. R., Frey, J., Zhobro, M., Levina, A. &amp; Martius, G. Learning with 3D rotations, a hitchhiker\u2019s guide to SO(3). In Proc. 41st International Conference on Machine Learning Vol. 235 (eds Salakhutdinov, R. et al.) 15331\u201315350 (PMLR, 2024).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR34\">Lin, C., Hanson, A. J. &amp; Hanson, S. M. Algebraically rigorous quaternion framework for the neural network pose estimation problem. In Proc. IEEE\/CVF International Conference on Computer Vision 14097\u201314106 (IEEE, 2023).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR35\">He, K., Zhang, X., Ren, S. &amp; Sun, J. Deep residual learning for image recognition. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 770\u2013778 (IEEE, 2016).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR36\">Flepp, R. et al. Automatic multi-view X-ray\/CT registration using bone substructure contours. Int. J. Comput. Assist. Radiol. Surg. 20, 1401\u20131408 (2025).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"noopener nofollow\" data-track-label=\"10.1007\/s11548-025-03391-4\" data-track-item_id=\"10.1007\/s11548-025-03391-4\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/link.springer.com\/doi\/10.1007\/s11548-025-03391-4\" aria-label=\"Article reference 36\" data-doi=\"10.1007\/s11548-025-03391-4\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=40394451\" aria-label=\"PubMed reference 36\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 36\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Automatic%20multi-view%20X-ray%2FCT%20registration%20using%20bone%20substructure%20contours&amp;journal=Int.%20J.%20Comput.%20Assist.%20Radiol.%20Surg.&amp;doi=10.1007%2Fs11548-025-03391-4&amp;volume=20&amp;pages=1401-1408&amp;publication_year=2025&amp;author=Flepp%2CR\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR37\">Mitrovi\u0107, U., \u0160piclin, \u017d, Likar, B. &amp; Pernu\u0161, F. 3D-2D registration of cerebral angiograms: a method and evaluation on clinical images. IEEE Trans. Med. Imaging 32, 1550\u20131563 (2013).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1109\/TMI.2013.2259844\" data-track-item_id=\"10.1109\/TMI.2013.2259844\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1109%2FTMI.2013.2259844\" aria-label=\"Article reference 37\" data-doi=\"10.1109\/TMI.2013.2259844\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"ads reference\" data-track-action=\"ads reference\" href=\"http:\/\/adsabs.harvard.edu\/cgi-bin\/nph-data_query?link_type=ABSTRACT&amp;bibcode=2013ITMI...32.1550M\" aria-label=\"ADS reference 37\" target=\"_blank\">ADS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=23649179\" aria-label=\"PubMed reference 37\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 37\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=3D-2D%20registration%20of%20cerebral%20angiograms%3A%20a%20method%20and%20evaluation%20on%20clinical%20images&amp;journal=IEEE%20Trans.%20Med.%20Imaging&amp;doi=10.1109%2FTMI.2013.2259844&amp;volume=32&amp;pages=1550-1563&amp;publication_year=2013&amp;author=Mitrovi%C4%87%2CU&amp;author=%C5%A0piclin%2C%C5%BD&amp;author=Likar%2CB&amp;author=Pernu%C5%A1%2CF\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR38\">Xu, M. et al. VesselBoost: a Python toolbox for small blood vessel segmentation in human magnetic resonance angiography data. Aperture Neuro <a href=\"https:\/\/doi.org\/10.52294\/001c.123217\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"10.52294\/001c.123217\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/doi.org\/10.52294\/001c.123217<\/a> (2024).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR39\">Ronneberger, O., Fischer, P. &amp; Brox, T. U-net: convolutional networks for biomedical image segmentation. In Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention (eds Navab, N. et al.) 234\u2013241 (Springer, 2015).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR40\">Terzakis, G. &amp; Lourakis, M. A consistently fast and globally optimal solution to the perspective-n-point problem. In Proc. European Conference on Computer Vision (eds Vedaldi, A. et al.) 478\u2013494 (Springer, 2020).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR41\">Potente, M. &amp; M\u00e4kinen, T. Vascular heterogeneity and specialization in development and disease. Nat. Rev. Mol. Cell Biol. 18, 477\u2013494 (2017).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/nrm.2017.36\" data-track-item_id=\"10.1038\/nrm.2017.36\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fnrm.2017.36\" aria-label=\"Article reference 41\" data-doi=\"10.1038\/nrm.2017.36\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BC2sXosVahurs%3D\" aria-label=\"CAS reference 41\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=28537573\" aria-label=\"PubMed reference 41\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 41\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Vascular%20heterogeneity%20and%20specialization%20in%20development%20and%20disease&amp;journal=Nat.%20Rev.%20Mol.%20Cell%20Biol.&amp;doi=10.1038%2Fnrm.2017.36&amp;volume=18&amp;pages=477-494&amp;publication_year=2017&amp;author=Potente%2CM&amp;author=M%C3%A4kinen%2CT\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR42\">Nu\u00f1ez, F. B. &amp; Dohna-Schwake, C. Epidemiology, diagnostics, and management of vein of Galen malformation. Pediatr. Neurol. 119, 50\u201355 (2021).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1016\/j.pediatrneurol.2021.02.007\" data-track-item_id=\"10.1016\/j.pediatrneurol.2021.02.007\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1016%2Fj.pediatrneurol.2021.02.007\" aria-label=\"Article reference 42\" data-doi=\"10.1016\/j.pediatrneurol.2021.02.007\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 42\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Epidemiology%2C%20diagnostics%2C%20and%20management%20of%20vein%20of%20Galen%20malformation&amp;journal=Pediatr.%20Neurol.&amp;doi=10.1016%2Fj.pediatrneurol.2021.02.007&amp;volume=119&amp;pages=50-55&amp;publication_year=2021&amp;author=Nu%C3%B1ez%2CFB&amp;author=Dohna-Schwake%2CC\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR43\">Varghese, C., Harrison, E. M., O\u2019Grady, G. &amp; Topol, E. J. Artificial intelligence in surgery. Nat. Med. 30, 1257\u20131268 (2024).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1038\/s41591-024-02970-3\" data-track-item_id=\"10.1038\/s41591-024-02970-3\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1038%2Fs41591-024-02970-3\" aria-label=\"Article reference 43\" data-doi=\"10.1038\/s41591-024-02970-3\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB2cXhtVGrs7fL\" aria-label=\"CAS reference 43\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=38740998\" aria-label=\"PubMed reference 43\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 43\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Artificial%20intelligence%20in%20surgery&amp;journal=Nat.%20Med.&amp;doi=10.1038%2Fs41591-024-02970-3&amp;volume=30&amp;pages=1257-1268&amp;publication_year=2024&amp;author=Varghese%2CC&amp;author=Harrison%2CEM&amp;author=O%E2%80%99Grady%2CG&amp;author=Topol%2CEJ\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR44\">Jena, R., Sethi, D., Chaudhari, P. &amp; Gee, J. Deep learning in medical image registration: magic or mirage? In Proc. 38th Annual Conference on Neural Information Processing Systems (eds Globerson, A. et al.) 108331\u2013108353 (Curran Associates Inc., 2024).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR45\">Dey, N. et al. Learning general-purpose biomedical volume representations using randomized synthesis. In Proc. 13th International Conference on Learning Representations (eds Yue, Y. et al.) (ICLR, 2025).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR46\">Gagoski, B. et al. Automated detection and reacquisition of motion-degraded images in fetal HASTE imaging at 3T. Magn. Reson. Med. 87, 1914\u20131922 (2022).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1002\/mrm.29106\" data-track-item_id=\"10.1002\/mrm.29106\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1002%2Fmrm.29106\" aria-label=\"Article reference 46\" data-doi=\"10.1002\/mrm.29106\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=34888942\" aria-label=\"PubMed reference 46\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 46\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Automated%20detection%20and%20reacquisition%20of%20motion-degraded%20images%20in%20fetal%20HASTE%20imaging%20at%203T&amp;journal=Magn.%20Reson.%20Med.&amp;doi=10.1002%2Fmrm.29106&amp;volume=87&amp;pages=1914-1922&amp;publication_year=2022&amp;author=Gagoski%2CB\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR47\">Arsigny, V., Commowick, O., Ayache, N. &amp; Pennec, X. A fast and log-euclidean polyaffine framework for locally linear registration. J. Math. Imaging Vis. 33, 222\u2013238 (2009).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"noopener nofollow\" data-track-label=\"10.1007\/s10851-008-0135-9\" data-track-item_id=\"10.1007\/s10851-008-0135-9\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/link.springer.com\/doi\/10.1007\/s10851-008-0135-9\" aria-label=\"Article reference 47\" data-doi=\"10.1007\/s10851-008-0135-9\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"mathscinet reference\" data-track-action=\"mathscinet reference\" href=\"http:\/\/www.ams.org\/mathscinet-getitem?mr=2480987\" aria-label=\"MathSciNet reference 47\" target=\"_blank\">MathSciNet<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 47\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=A%20fast%20and%20log-euclidean%20polyaffine%20framework%20for%20locally%20linear%20registration&amp;journal=J.%20Math.%20Imaging%20Vis.&amp;doi=10.1007%2Fs10851-008-0135-9&amp;volume=33&amp;pages=222-238&amp;publication_year=2009&amp;author=Arsigny%2CV&amp;author=Commowick%2CO&amp;author=Ayache%2CN&amp;author=Pennec%2CX\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR48\">Gopalakrishnan, V., Dey, N. &amp; Golland, P. PolyPose: deformable 2D\/3D registration via polyrigid transformations. In\u00a0Advances in Neural Information Processing Systems 60633\u201360659 (NeurIPS, 2025).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 48\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=PolyPose%3A%20deformable%202D%2F3D%20registration%20via%20polyrigid%20transformations&amp;journal=Advances%20in%20Neural%20Information%20Processing%20Systems&amp;volume=38&amp;pages=60633-60659&amp;publication_year=2026&amp;author=Gopalakrishnan%2CV&amp;author=Dey%2CN&amp;author=Golland%2CP\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR49\">Hartley, R. &amp; Zisserman, A. Multiple View Geometry in Computer Vision (Cambridge Univ. Press, 2003).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR50\">Swinehart, D. F. The Beer-Lambert law. J. Chem. Educ. 39, 333 (1962).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1021\/ed039p333\" data-track-item_id=\"10.1021\/ed039p333\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1021%2Fed039p333\" aria-label=\"Article reference 50\" data-doi=\"10.1021\/ed039p333\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DyaF38Xkt1KhtLc%3D\" aria-label=\"CAS reference 50\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 50\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=The%20Beer-Lambert%20law&amp;journal=J.%20Chem.%20Educ.&amp;doi=10.1021%2Fed039p333&amp;volume=39&amp;publication_year=1962&amp;author=Swinehart%2CDF\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR51\">Siddon, R. L. Fast calculation of the exact radiological path for a three-dimensional CT array. Med. Phys. 12, 252\u2013255 (1985).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.1118\/1.595715\" data-track-item_id=\"10.1118\/1.595715\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.1118%2F1.595715\" aria-label=\"Article reference 51\" data-doi=\"10.1118\/1.595715\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:STN:280:DyaL2M3htlaktQ%3D%3D\" aria-label=\"CAS reference 51\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"pubmed reference\" data-track-action=\"pubmed reference\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Abstract&amp;list_uids=4000088\" aria-label=\"PubMed reference 51\" target=\"_blank\">PubMed<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 51\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Fast%20calculation%20of%20the%20exact%20radiological%20path%20for%20a%20three-dimensional%20CT%20array&amp;journal=Med.%20Phys.&amp;doi=10.1118%2F1.595715&amp;volume=12&amp;pages=252-255&amp;publication_year=1985&amp;author=Siddon%2CRL\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR52\">Unberath, M. et al. DeepDRR\u2014a catalyst for machine learning in fluoroscopy-guided procedures. In Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention (eds Frangi, A. F. et al.) 98\u2013106 (Springer, 2018).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR53\">Milletari, F., Navab, N. &amp; Ahmadi, S.-A. V-net: fully convolutional neural networks for volumetric medical image segmentation. In Proc. 2016 Fourth International Conference on 3D Vision (3DV) 565\u2013571 (IEEE, 2016).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR54\">Grupp, R. B., Armand, M. &amp; Taylor, R. H. Patch-based image similarity for intraoperative 2D\/3D pelvis registration during periacetabular osteotomy. In Proc. International Workshop on Computer-Assisted and Robotic Endoscopy 153\u2013163 (Springer, 2018).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR55\">Ferrara, D. et al. Sharing a whole-\/total-body [18F] FDG-PET\/CT dataset with CT-derived segmentations: an ENHANCE.PET initiative. Sci. Data 13, 869 (2026).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR56\">Sundar, L. K. S. et al. Fully automated, semantic segmentation of whole-body 18F-FDG PET\/CT images based on data-centric artificial intelligence. J. Nucl. Med. 63, 1941\u20131948 (2022).<\/p>\n<p class=\"c-article-references__links u-hide-print\"><a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"10.2967\/jnumed.122.264063\" data-track-item_id=\"10.2967\/jnumed.122.264063\" data-track-value=\"article reference\" data-track-action=\"article reference\" href=\"https:\/\/doi.org\/10.2967%2Fjnumed.122.264063\" aria-label=\"Article reference 56\" data-doi=\"10.2967\/jnumed.122.264063\" target=\"_blank\">Article<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" rel=\"nofollow noopener\" data-track-label=\"link\" data-track-item_id=\"link\" data-track-value=\"cas reference\" data-track-action=\"cas reference\" href=\"https:\/\/www.nature.com\/articles\/cas-redirect\/1:CAS:528:DC%2BB3sXnt1KmsQ%3D%3D\" aria-label=\"CAS reference 56\" target=\"_blank\">CAS<\/a>\u00a0<br \/>\n    <a data-track=\"click_references\" data-track-action=\"google scholar reference\" data-track-value=\"google scholar reference\" data-track-label=\"link\" data-track-item_id=\"link\" rel=\"nofollow noopener\" aria-label=\"Google Scholar reference 56\" href=\"http:\/\/scholar.google.com\/scholar_lookup?&amp;title=Fully%20automated%2C%20semantic%20segmentation%20of%20whole-body%2018F-FDG%20PET%2FCT%20images%20based%20on%20data-centric%20artificial%20intelligence&amp;journal=J.%20Nucl.%20Med.&amp;doi=10.2967%2Fjnumed.122.264063&amp;volume=63&amp;pages=1941-1948&amp;publication_year=2022&amp;author=Sundar%2CLKS\" target=\"_blank\"><br \/>\n                    Google Scholar<\/a>\u00a0\n                <\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR57\">Grupp, R. et al. Data and code associated with the publication: Automatic annotation of hip anatomy in fluoroscopy for robust and efficient 2D\/3D registration. Johns Hopkins Research Data Repository <a href=\"https:\/\/doi.org\/10.7281\/T1\/IFSXNV\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"10.7281\/T1\/IFSXNV\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/doi.org\/10.7281\/T1\/IFSXNV<\/a> (2020).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR58\">Flepp, R. et al.\u00a0Automatic multi-View X-Ray\/CT registration using bone substructure contours. Zenodo <a href=\"https:\/\/zenodo.org\/records\/15753063\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/zenodo.org\/records\/15753063\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/zenodo.org\/records\/15753063<\/a> (2025).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR59\">Mitrovi\u0107, U. &amp; \u0160piclin, \u017d. Gold standard for 3D-2D registration of cerebral angiograms. Laboratory of Imaging Technologies <a href=\"https:\/\/lit.fe.uni-lj.si\/en\/research\/resources\/3D-2D-GS-CA\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"https:\/\/lit.fe.uni-lj.si\/en\/research\/resources\/3D-2D-GS-CA\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/lit.fe.uni-lj.si\/en\/research\/resources\/3D-2D-GS-CA<\/a> (2013).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR60\">Pengbo, L. et al. CTPelvic1K Dataset. Zenodo <a href=\"https:\/\/doi.org\/10.5281\/zenodo.4588402\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"10.5281\/zenodo.4588402\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/doi.org\/10.5281\/zenodo.4588402<\/a> (2020).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR61\">Wasserthal, J. Dataset with segmentations of 117 important anatomical structures in 1228 CT images. Zenodo <a href=\"https:\/\/doi.org\/10.5281\/zenodo.6802613\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"10.5281\/zenodo.6802613\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/doi.org\/10.5281\/zenodo.6802613<\/a> (2023).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR62\">Ferrara, D. et al. ENHANCE.PET 1.6k: a whole-\/total-body [18F]FDG-PET\/CT dataset with CT-derived segmentations. Science Data Bank <a href=\"https:\/\/doi.org\/10.57760\/sciencedb.34150\" data-track=\"click_references\" data-track-action=\"external reference\" data-track-value=\"external reference\" data-track-label=\"10.57760\/sciencedb.34150\" rel=\"nofollow noopener\" target=\"_blank\">https:\/\/doi.org\/10.57760\/sciencedb.34150<\/a> (2026).<\/p>\n<p class=\"c-article-references__text\" id=\"ref-CR63\">Paszke, A. et al. PyTorch: an imperative style, high-performance deep learning library. In Proc. 33rd International Conference on Neural Information Processing Systems (eds Wallach, H. M. et al.) 8026\u20138037 (Curran Associates Inc., 2019).<\/p>\n","protected":false},"excerpt":{"rendered":"Unberath, M. et al. 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