Customized Efficient Neural Network for COVID-19 Infected Region Identification in CT Images
Background: In the field of biomedical imaging, radiomics is a promising approach that aims to provide quantitative features from images. It is highly dependent on accurate identification and delineation of the volume of interest to avoid mistakes in the implementation of the texture-based predictio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/7/8/131 |
| _version_ | 1827685057568964608 |
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| author | Alessandro Stefano Albert Comelli |
| author_facet | Alessandro Stefano Albert Comelli |
| author_sort | Alessandro Stefano |
| collection | DOAJ |
| description | Background: In the field of biomedical imaging, radiomics is a promising approach that aims to provide quantitative features from images. It is highly dependent on accurate identification and delineation of the volume of interest to avoid mistakes in the implementation of the texture-based prediction model. In this context, 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. Methods: In a previous study, 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. 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. Results: 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. Conclusions: 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. |
| first_indexed | 2024-03-10T08:41:43Z |
| format | Article |
| id | doaj.art-2e13f0b59f4347a88d106a2236764ec3 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-10T08:41:43Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-2e13f0b59f4347a88d106a2236764ec32023-11-22T08:13:50ZengMDPI AGJournal of Imaging2313-433X2021-08-017813110.3390/jimaging7080131Customized Efficient Neural Network for COVID-19 Infected Region Identification in CT ImagesAlessandro Stefano0Albert Comelli1Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, ItalyRi.MED Foundation, 90133 Palermo, ItalyBackground: In the field of biomedical imaging, radiomics is a promising approach that aims to provide quantitative features from images. It is highly dependent on accurate identification and delineation of the volume of interest to avoid mistakes in the implementation of the texture-based prediction model. In this context, 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. Methods: In a previous study, 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. 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. Results: 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. Conclusions: 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.https://www.mdpi.com/2313-433X/7/8/131COVID-19deep learningsegmentationcomputed tomographycustomized ENET |
| spellingShingle | Alessandro Stefano Albert Comelli Customized Efficient Neural Network for COVID-19 Infected Region Identification in CT Images Journal of Imaging COVID-19 deep learning segmentation computed tomography customized ENET |
| title | Customized Efficient Neural Network for COVID-19 Infected Region Identification in CT Images |
| title_full | Customized Efficient Neural Network for COVID-19 Infected Region Identification in CT Images |
| title_fullStr | Customized Efficient Neural Network for COVID-19 Infected Region Identification in CT Images |
| title_full_unstemmed | Customized Efficient Neural Network for COVID-19 Infected Region Identification in CT Images |
| title_short | Customized Efficient Neural Network for COVID-19 Infected Region Identification in CT Images |
| title_sort | customized efficient neural network for covid 19 infected region identification in ct images |
| topic | COVID-19 deep learning segmentation computed tomography customized ENET |
| url | https://www.mdpi.com/2313-433X/7/8/131 |
| work_keys_str_mv | AT alessandrostefano customizedefficientneuralnetworkforcovid19infectedregionidentificationinctimages AT albertcomelli customizedefficientneuralnetworkforcovid19infectedregionidentificationinctimages |