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Article

An Open-Source COVID-19 CT Dataset with Automatic Lung Tissue Classification for Radiomics

1
Department of Experimental and Clinical Medicine, University “Magna Graecia” of Catanzaro, 88100 Catanzaro, Italy
2
Department of Mathematics and Computer Science, University of Calabria, 87036 Rende, Italy
3
Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy
4
Department of Advanced Robotics, Istituito Italiano di Tecnologia, 16163 Genova, Italy
5
Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, 20133 Milano, Italy
6
Department of Radiology, Pugliese-Ciaccio Hospital, 88100 Catanzaro, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Anthony Guiseppi-Elie
Bioengineering 2021, 8(2), 26; https://doi.org/10.3390/bioengineering8020026
Received: 22 December 2020 / Revised: 22 January 2021 / Accepted: 12 February 2021 / Published: 16 February 2021
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. View Full-Text
Keywords: COVID-19; free CT dataset; medical imaging; radiomics COVID-19; free CT dataset; medical imaging; radiomics
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MDPI and ACS Style

Zaffino, P.; Marzullo, A.; Moccia, S.; Calimeri, F.; De Momi, E.; Bertucci, B.; Arcuri, P.P.; Spadea, M.F. An Open-Source COVID-19 CT Dataset with Automatic Lung Tissue Classification for Radiomics. Bioengineering 2021, 8, 26. https://doi.org/10.3390/bioengineering8020026

AMA Style

Zaffino P, Marzullo A, Moccia S, Calimeri F, De Momi E, Bertucci B, Arcuri PP, Spadea MF. An Open-Source COVID-19 CT Dataset with Automatic Lung Tissue Classification for Radiomics. Bioengineering. 2021; 8(2):26. https://doi.org/10.3390/bioengineering8020026

Chicago/Turabian Style

Zaffino, Paolo, Aldo Marzullo, Sara Moccia, Francesco Calimeri, Elena De Momi, Bernardo Bertucci, Pier P. Arcuri, and Maria F. Spadea. 2021. "An Open-Source COVID-19 CT Dataset with Automatic Lung Tissue Classification for Radiomics" Bioengineering 8, no. 2: 26. https://doi.org/10.3390/bioengineering8020026

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