Automated Lung Segmentation from Computed Tomography Images of Normal and COVID-19 Pneumonia Patients

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Gholamiankhah Faeze; Mostafapour Samaneh; Abdi Goushbolagh Nouraddin; Shojaerazavi Seyedjafar; LAYEGH PARVANEH; TABATABAEI SEYYED MOHAMMAD; ARABI HOSSEIN

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Journal: IRANIAN JOURNAL OF MEDICAL SCIENCES (IJMS) Year:2022 | Volume:47 | Issue:5 Page(s): 440-449

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Title

Automated Lung Segmentation from Computed Tomography Images of Normal and COVID-19 Pneumonia Patients

Author(s)

Keywords

COVID-19

Lung

Computed tomography

X-ray

Image processing

Computer-assisted

Deep learning

Abstract

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 MannWhitney 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).

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APA: Copy

Gholamiankhah, Faeze, Mostafapour, Samaneh, Abdi Goushbolagh, Nouraddin, Shojaerazavi, Seyedjafar, LAYEGH, PARVANEH, TABATABAEI, SEYYED MOHAMMAD, & ARABI, HOSSEIN. (2022). Automated Lung Segmentation from Computed Tomography Images of Normal and COVID-19 Pneumonia Patients. IRANIAN JOURNAL OF MEDICAL SCIENCES (IJMS), 47(5), 440-449. SID. https://sid.ir/paper/1091440/en

Vancouver: Copy

Gholamiankhah Faeze, Mostafapour Samaneh, Abdi Goushbolagh Nouraddin, Shojaerazavi Seyedjafar, LAYEGH PARVANEH, TABATABAEI SEYYED MOHAMMAD, ARABI HOSSEIN. Automated Lung Segmentation from Computed Tomography Images of Normal and COVID-19 Pneumonia Patients. IRANIAN JOURNAL OF MEDICAL SCIENCES (IJMS)[Internet]. 2022;47(5):440-449. Available from: https://sid.ir/paper/1091440/en

IEEE: Copy

Faeze Gholamiankhah, Samaneh Mostafapour, Nouraddin Abdi Goushbolagh, Seyedjafar Shojaerazavi, PARVANEH LAYEGH, SEYYED MOHAMMAD TABATABAEI, and HOSSEIN ARABI, “Automated Lung Segmentation from Computed Tomography Images of Normal and COVID-19 Pneumonia Patients,” IRANIAN JOURNAL OF MEDICAL SCIENCES (IJMS), vol. 47, no. 5, pp. 440–449, 2022, [Online]. Available: https://sid.ir/paper/1091440/en

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