{"id":457,"date":"2021-01-02T09:09:43","date_gmt":"2021-01-02T09:09:43","guid":{"rendered":"https:\/\/www.pi.infn.it\/aim\/?page_id=457"},"modified":"2022-04-11T10:14:11","modified_gmt":"2022-04-11T09:14:11","slug":"aim-covid19-wg","status":"publish","type":"page","link":"https:\/\/www.pi.infn.it\/aim\/aim-covid19-wg\/","title":{"rendered":"AIM-Covid19-WG"},"content":{"rendered":"\n<div class=\"wp-block-themeisle-blocks-advanced-columns has-1-columns has-desktop-equal-layout has-tablet-equal-layout has-mobile-equal-layout has-default-gap has-vertical-unset\" id=\"wp-block-themeisle-blocks-advanced-columns-956d452a\"><div class=\"wp-block-themeisle-blocks-advanced-columns-overlay\"><\/div><div class=\"innerblocks-wrap\">\n<div class=\"wp-block-themeisle-blocks-advanced-column\" id=\"wp-block-themeisle-blocks-advanced-column-974c7a78\">\n<p>The typical findings detected in the lung CT of patients affected by Covid-19 pneumonia consists in a pattern of infection with typical features including: ground glass opacities (GGO) in the lung periphery, rounded opacities, consolidations that are a sign of progressing critical illness.<\/p>\n\n\n\n<p>The radiographic presentation is similar to that of other influenza-associated pneumonias.<\/p>\n\n\n\n<p>Techniques for distinguishing between these differently originated pneumonias may strengthen support toward use of CT and automated image analysis in diagnostic settings.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" src=\"https:\/\/www.pi.infn.it\/aim\/wp-content\/uploads\/2021\/01\/covid-1024x315.png\" alt=\"\"\/><\/figure>\n\n\n\n<p>Quantitative information on the amount of GG opacities and their distribution, possibly combined with clinical and epidemiological patient\u2019s information, may be relevant to set up predictive models for patients\u2019 stratification, prognosis prediction, etc.<\/p>\n\n\n\n<p>Quantification modules, once properly validated, could be valuable tools for clinicians to set up large-scale population studies.<\/p>\n\n\n\n<p>A segmentation pipeline based on deep neural network has been developed, according to the following scheme.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"318\" src=\"https:\/\/www.pi.infn.it\/aim\/wp-content\/uploads\/2022\/04\/LungQuant-1024x318.png\" alt=\"\" class=\"wp-image-939\" srcset=\"https:\/\/www.pi.infn.it\/aim\/wp-content\/uploads\/2022\/04\/LungQuant-1024x318.png 1024w, https:\/\/www.pi.infn.it\/aim\/wp-content\/uploads\/2022\/04\/LungQuant-300x93.png 300w, https:\/\/www.pi.infn.it\/aim\/wp-content\/uploads\/2022\/04\/LungQuant-768x238.png 768w, https:\/\/www.pi.infn.it\/aim\/wp-content\/uploads\/2022\/04\/LungQuant-1536x477.png 1536w, https:\/\/www.pi.infn.it\/aim\/wp-content\/uploads\/2022\/04\/LungQuant-2048x636.png 2048w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p>See the paper by Lizzi, F., Agosti, A., Brero, F., Cabini, R. F., Fantacci, M. E., Figini, S., \u2026 Retico, A. (2021). Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade of two U-nets: training and assessment on multiple datasets using different annotation criteria. <em>IJCARS 2021. https:\/\/doi.org\/10.1007\/s11548-021-02501-2<\/em><\/p>\n<\/div>\n<\/div><\/div>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"neve_meta_sidebar":"full-width","neve_meta_container":"","neve_meta_enable_content_width":"on","neve_meta_content_width":100,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","_ti_tpc_template_sync":false,"_ti_tpc_template_id":"","footnotes":""},"_links":{"self":[{"href":"https:\/\/www.pi.infn.it\/aim\/wp-json\/wp\/v2\/pages\/457"}],"collection":[{"href":"https:\/\/www.pi.infn.it\/aim\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.pi.infn.it\/aim\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.pi.infn.it\/aim\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.pi.infn.it\/aim\/wp-json\/wp\/v2\/comments?post=457"}],"version-history":[{"count":9,"href":"https:\/\/www.pi.infn.it\/aim\/wp-json\/wp\/v2\/pages\/457\/revisions"}],"predecessor-version":[{"id":940,"href":"https:\/\/www.pi.infn.it\/aim\/wp-json\/wp\/v2\/pages\/457\/revisions\/940"}],"wp:attachment":[{"href":"https:\/\/www.pi.infn.it\/aim\/wp-json\/wp\/v2\/media?parent=457"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}