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10.3390/bioengineering8020026 http://scihub22266oqcxt.onion/10.3390/bioengineering8020026 33669235!7919807!33669235     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Bioengineering+(Basel) 2021 ; 8 (2): ä Nephropedia Template TP {{tp|p=33669235|t=2021. An Open-Source COVID-19 CT Dataset with Automatic Lung Tissue Classification for Radiomics |pdf=|usr=}}{{33669235}} gab.com Text An Open-Source COVID-19 CT Dataset with Automatic Lung Tissue Classification for Radiomics JO: Bioengineering (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=33669235 #FOAvip The coronavirus disease 19 (COVID-19) pandemic is having a dramatic impact on society and healthcare systems. In this complex scenario, lung computerized tomography (CT) may play an important prognostic role. However, datasets released so far present limitations that hamper the development of tools for quantitative analysis. In this paper, we present an open-source lung CT dataset comprising information on 50 COVID-19-positive patients. The CT volumes are provided along with (i) an automatic threshold-based annotation obtained with a Gaussian mixture model (GMM) and (ii) a scoring provided by an expert radiologist. This score was found to significantly correlate with the presence of ground glass opacities and the consolidation found with GMM. The dataset is freely available in an ITK-based file format under the CC BY-NC 4.0 license. The code for GMM fitting is publicly available, as well. We believe that our dataset will provide a unique opportunity for researchers working in the field of medical image analysis, and hope that its release will lay the foundations for the successfully implementation of algorithms to support clinicians in facing the COVID-19 pandemic. Twit Text FOAVip An Open-Source COVID-19 CT Dataset with Automatic Lung Tissue Classification for Radiomics ????Bioengineering (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=33669235 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33669235 #FOAvip English Wikipedia <ref>{{Cite pmid|33669235}}</ref> An Open-Source COVID-19 CT Dataset with Automatic Lung Tissue Classification for Radiomics #MMPMID33669235 Zaffino P; Marzullo A; Moccia S; Calimeri F; De Momi E; Bertucci B; Arcuri PP; Spadea MF Bioengineering (Basel) 2021[Feb]; 8 (2): ä PMID33669235show ga The coronavirus disease 19 (COVID-19) pandemic is having a dramatic impact on society and healthcare systems. In this complex scenario, lung computerized tomography (CT) may play an important prognostic role. However, datasets released so far present limitations that hamper the development of tools for quantitative analysis. In this paper, we present an open-source lung CT dataset comprising information on 50 COVID-19-positive patients. The CT volumes are provided along with (i) an automatic threshold-based annotation obtained with a Gaussian mixture model (GMM) and (ii) a scoring provided by an expert radiologist. This score was found to significantly correlate with the presence of ground glass opacities and the consolidation found with GMM. The dataset is freely available in an ITK-based file format under the CC BY-NC 4.0 license. The code for GMM fitting is publicly available, as well. We believe that our dataset will provide a unique opportunity for researchers working in the field of medical image analysis, and hope that its release will lay the foundations for the successfully implementation of algorithms to support clinicians in facing the COVID-19 pandemic. äAn Open-Source COVID-19 CT Dataset with Automatic Lung Tissue Classifi(epmc).pdf DeepDyve Pubget Overpricing