We present a customized deep learning approach aimed at addressing the real-time, and fully automated identification and segmentation of COVID-19 infected regions in computed tomography images. In a previous study (https:\/\/doi.org\/10.3390\/jimaging6110125), we adopted ENET, originally used for image segmentation tasks in self-driving cars, for whole parenchyma segmentation in patients with idiopathic pulmonary fibrosis which has several similarities to COVID-19 disease. <\/p>\n\n\n\n
To automatically identify and segment COVID-19 infected areas, a customized ENET, namely C-ENET, was implemented and its performance compared to the original ENET and some state-of-the-art deep learning architectures. <\/p>\n\n\n\n
The experimental results demonstrate the effectiveness of our approach. Considering the performance obtained in terms of similarity of the result of the segmentation to the gold standard (dice similarity coefficient ~75%), our proposed methodology can be used for the identification and delineation of COVID-19 infected areas without any supervision of a radiologist, in order to obtain a volume of interest independent from the user. <\/p>\n\n\n\n
We demonstrated that the proposed customized deep learning model can be applied to rapidly identify, and segment COVID-19 infected regions to subsequently extract useful information for assessing disease severity through radiomics analyses.<\/p>\n\n\n\n
Authors<\/strong>: Alessandro Stefano and Albert Comelli.<\/p>\n\n\n\n Published paper<\/strong>: Stefano, A. and Comelli, A., Customized efficient neural network for covid-19 infected region identification in CT images<\/a>. J. Imaging 2021, 7. 10.3390\/jimaging7080131<\/p>\n","protected":false},"excerpt":{"rendered":" We present a customized deep learning approach aimed at addressing the real-time, and fully automated identification and segmentation of COVID-19 infected regions in computed tomography images. In a previous study (https:\/\/doi.org\/10.3390\/jimaging6110125), we adopted ENET, originally used for image segmentation tasks in self-driving cars, for whole parenchyma segmentation in patients with idiopathic pulmonary fibrosis which has… Read More »Customized Efficient Neural Network for COVID-19 Infected Region Identification in CT Images<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":883,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"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":""},"categories":[1],"tags":[37,39,34,40,4,38],"_links":{"self":[{"href":"https:\/\/www.pi.infn.it\/aim\/wp-json\/wp\/v2\/posts\/882"}],"collection":[{"href":"https:\/\/www.pi.infn.it\/aim\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.pi.infn.it\/aim\/wp-json\/wp\/v2\/types\/post"}],"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=882"}],"version-history":[{"count":1,"href":"https:\/\/www.pi.infn.it\/aim\/wp-json\/wp\/v2\/posts\/882\/revisions"}],"predecessor-version":[{"id":884,"href":"https:\/\/www.pi.infn.it\/aim\/wp-json\/wp\/v2\/posts\/882\/revisions\/884"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.pi.infn.it\/aim\/wp-json\/wp\/v2\/media\/883"}],"wp:attachment":[{"href":"https:\/\/www.pi.infn.it\/aim\/wp-json\/wp\/v2\/media?parent=882"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.pi.infn.it\/aim\/wp-json\/wp\/v2\/categories?post=882"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.pi.infn.it\/aim\/wp-json\/wp\/v2\/tags?post=882"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}