Automated Lung Segmentation from Computed Tomography Images of Normal and COVID-19 Pneumonia Patients
| Autor: | Faeze Gholamiankhah, Samaneh Mostafapour, Nouraddin Abdi Goushbolagh, Seyedjafar Shojaerazavi, Parvaneh Layegh, Seyyed Mohammad Tabatabaei, Hossein Arabi |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Iranian Journal of Medical Sciences, Vol 47, Iss 5, Pp 440-449 (2022) |
| Druh dokumentu: | article |
| ISSN: | 0253-0716 1735-3688 |
| DOI: | 10.30476/ijms.2022.90791.2178 |
| Popis: | Background: Automated image segmentation is an essential step in quantitative image analysis. This study assesses the performance of a deep learning-based model for lung segmentation from computed tomography (CT) images of normal and COVID-19 patients. Methods: A descriptive-analytical study was conducted from December 2020 to April 2021 on the CT images of patients from various educational hospitals affiliated with Mashhad University of Medical Sciences (Mashhad, Iran). Of the selected images and corresponding lung masks of 1,200 confirmed COVID-19 patients, 1,080 were used to train a residual neural network. The performance of the residual network (ResNet) model was evaluated on two distinct external test datasets, namely the remaining 120 COVID-19 and 120 normal patients. Different evaluation metrics such as Dice similarity coefficient (DSC), mean absolute error (MAE), relative mean Hounsfield unit (HU) difference, and relative volume difference were calculated to assess the accuracy of the predicted lung masks. The Mann-Whitney U test was used to assess the difference between the corresponding values in the normal and COVID-19 patients. P |
| Databáze: | Directory of Open Access Journals |
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