Automated Lung Segmentation from Computed Tomography Images of Normal and COVID-19 Pneumonia Patients | ||
| Iranian Journal of Medical Sciences | ||
| مقاله 6، دوره 47، شماره 5، آذر 2022، صفحه 440-449 اصل مقاله (2.21 M) | ||
| نوع مقاله: Original Article(s) | ||
| شناسه دیجیتال (DOI): 10.30476/ijms.2022.90791.2178 | ||
| نویسندگان | ||
| Faeze Gholamiankhah1؛ Samaneh Mostafapour2؛ Nouraddin Abdi Goushbolagh1؛ Seyedjafar Shojaerazavi3؛ Parvaneh Layegh4؛ Seyyed Mohammad Tabatabaei5، 6؛ Hossein Arabi* 7 | ||
| 1Department of Medical Physics, School of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran | ||
| 2Department of Radiology Technology, School of Paramedical Sciences, Mashhad University of Sciences, Yazd, Iran | ||
| 3Department of Cardiology, Ghaem Hospital, Mashhad University of Medical Sciences, Mashhad, Iran | ||
| 4Department of Radiology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran | ||
| 5Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran | ||
| 6Clinical Research Development Unit, Imam Reza Hospital, Mashhad University of Medical Sciences, Mashhad, Iran | ||
| 7Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland | ||
| چکیده | ||
| 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<0.05 was considered statistically significant. Results: The ResNet model achieved a DSC of 0.980 and 0.971 and a relative mean HU difference of -2.679% and -4.403% for the normal and COVID-19 patients, respectively. Comparable performance in lung segmentation of normal and COVID-19 patients indicated the model’s accuracy for identifying lung tissue in the presence of COVID-19-associated infections. Although a slightly better performance was observed in normal patients. Conclusion: The ResNet model provides an accurate and reliable automated lung segmentation of COVID-19 infected lung tissue. A preprint version of this article was published on arXiv before formal peer review (https://arxiv.org/abs/2104.02042). | ||
| کلیدواژهها | ||
| COVID-19؛ Lung؛ Computed tomography؛ X-ray؛ Image processing؛ Computer-assisted؛ Deep learning | ||
| مراجع | ||
|
| ||
|
آمار تعداد مشاهده مقاله: 11,954 تعداد دریافت فایل اصل مقاله: 3,669 |
||