tetano
Editor, Senior Moderator
Sci Rep
. 2022 Feb 25;12(1):3212.
doi: 10.1038/s41598-022-06854-9.
COVID-rate: an automated framework for segmentation of COVID-19 lesions from chest CT images
Nastaran Enshaei[SUP] 1 [/SUP], Anastasia Oikonomou[SUP] 2 [/SUP], Moezedin Javad Rafiee[SUP] 3 [/SUP], Parnian Afshar[SUP] 1 [/SUP], Shahin Heidarian[SUP] 4 [/SUP], Arash Mohammadi[SUP] 1 [/SUP], Konstantinos N Plataniotis[SUP] 5 [/SUP], Farnoosh Naderkhani[SUP] 1 [/SUP]
Affiliations
Abstract
Novel Coronavirus disease (COVID-19) is a highly contagious respiratory infection that has had devastating effects on the world. Recently, new COVID-19 variants are emerging making the situation more challenging and threatening. Evaluation and quantification of COVID-19 lung abnormalities based on chest Computed Tomography (CT) images can help determining the disease stage, efficiently allocating limited healthcare resources, and making informed treatment decisions. During pandemic era, however, visual assessment and quantification of COVID-19 lung lesions by expert radiologists become expensive and prone to error, which raises an urgent quest to develop practical autonomous solutions. In this context, first, the paper introduces an open-access COVID-19 CT segmentation dataset containing 433 CT images from 82 patients that have been annotated by an expert radiologist. Second, a Deep Neural Network (DNN)-based framework is proposed, referred to as the [Formula: see text], that autonomously segments lung abnormalities associated with COVID-19 from chest CT images. Performance of the proposed [Formula: see text] framework is evaluated through several experiments based on the introduced and external datasets. Third, an unsupervised enhancement approach is introduced that can reduce the gap between the training set and test set and improve the model generalization. The enhanced results show a dice score of 0.8069 and specificity and sensitivity of 0.9969 and 0.8354, respectively. Furthermore, the results indicate that the [Formula: see text] model can efficiently segment COVID-19 lesions in both 2D CT images and whole lung volumes. Results on the external dataset illustrate generalization capabilities of the [Formula: see text] model to CT images obtained from a different scanner.
. 2022 Feb 25;12(1):3212.
doi: 10.1038/s41598-022-06854-9.
COVID-rate: an automated framework for segmentation of COVID-19 lesions from chest CT images
Nastaran Enshaei[SUP] 1 [/SUP], Anastasia Oikonomou[SUP] 2 [/SUP], Moezedin Javad Rafiee[SUP] 3 [/SUP], Parnian Afshar[SUP] 1 [/SUP], Shahin Heidarian[SUP] 4 [/SUP], Arash Mohammadi[SUP] 1 [/SUP], Konstantinos N Plataniotis[SUP] 5 [/SUP], Farnoosh Naderkhani[SUP] 1 [/SUP]
Affiliations
- PMID: 35217712
- DOI: 10.1038/s41598-022-06854-9
Abstract
Novel Coronavirus disease (COVID-19) is a highly contagious respiratory infection that has had devastating effects on the world. Recently, new COVID-19 variants are emerging making the situation more challenging and threatening. Evaluation and quantification of COVID-19 lung abnormalities based on chest Computed Tomography (CT) images can help determining the disease stage, efficiently allocating limited healthcare resources, and making informed treatment decisions. During pandemic era, however, visual assessment and quantification of COVID-19 lung lesions by expert radiologists become expensive and prone to error, which raises an urgent quest to develop practical autonomous solutions. In this context, first, the paper introduces an open-access COVID-19 CT segmentation dataset containing 433 CT images from 82 patients that have been annotated by an expert radiologist. Second, a Deep Neural Network (DNN)-based framework is proposed, referred to as the [Formula: see text], that autonomously segments lung abnormalities associated with COVID-19 from chest CT images. Performance of the proposed [Formula: see text] framework is evaluated through several experiments based on the introduced and external datasets. Third, an unsupervised enhancement approach is introduced that can reduce the gap between the training set and test set and improve the model generalization. The enhanced results show a dice score of 0.8069 and specificity and sensitivity of 0.9969 and 0.8354, respectively. Furthermore, the results indicate that the [Formula: see text] model can efficiently segment COVID-19 lesions in both 2D CT images and whole lung volumes. Results on the external dataset illustrate generalization capabilities of the [Formula: see text] model to CT images obtained from a different scanner.