{"id":45971,"date":"2023-11-20T05:47:25","date_gmt":"2023-11-20T11:47:25","guid":{"rendered":"https:\/\/stage.cancerimagingarchive.net\/tcia-citation\/plethora-pub\/"},"modified":"2023-11-20T05:47:25","modified_gmt":"2023-11-20T11:47:25","slug":"plethora-pub","status":"publish","type":"tcia_citation","link":"https:\/\/stage.cancerimagingarchive.net\/tcia-citation\/plethora-pub\/","title":{"rendered":"PLETHORA-PUB"},"template":"","class_list":["post-45971","tcia_citation","type-tcia_citation","status-publish"],"tcia_citation_type":"Publication Citation","tcia_citation_text":"<p>Kiser, K.J., Barman, A., Stieb, S., Fuller, C.D., Giancardo, L., 2021. <strong>Novel Autosegmentation Spatial Similarity Metrics Capture the Time Required to Correct Segmentations Better Than Traditional Metrics in a Thoracic Cavity Segmentation Workflow<\/strong>. J Digit Imaging. <a href=\"https:\/\/doi.org\/10.1007\/s10278-021-00460-3\">https:\/\/doi.org\/10.1007\/s10278-021-00460-3<\/a>\u00a0 <br \/>PMID:\u00a034027588\u00a0 PMCID:\u00a0PMC8329111 <br \/>(2020 medRxiv preprint doi):\u00a0<span>https:\/\/doi.org\/10.1101\/2020.05.14.20102103<\/span>.<\/p>\n","tcia_citation_statement":"","tcia_citation_doi":"10.1007\/s10278-021-00460-3","_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/citations\/45971","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/citations"}],"about":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/types\/tcia_citation"}],"wp:attachment":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media?parent=45971"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}