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Gholamiankhah, Faeze, Mostafapour, Samaneh, Abdi Goushbolagh, Nouraddin, Shojaerazavi, Seyedjafar, Layegh, Parvaneh, Tabatabaei, Seyyed Mohammad, Arabi, Hossein. (1401). Automated Lung Segmentation from Computed Tomography Images of Normal and COVID-19 Pneumonia Patients. سامانه مدیریت نشریات علمی, 47(5), 440-449. doi: 10.30476/ijms.2022.90791.2178
Faeze Gholamiankhah; Samaneh Mostafapour; Nouraddin Abdi Goushbolagh; Seyedjafar Shojaerazavi; Parvaneh Layegh; Seyyed Mohammad Tabatabaei; Hossein Arabi. "Automated Lung Segmentation from Computed Tomography Images of Normal and COVID-19 Pneumonia Patients". سامانه مدیریت نشریات علمی, 47, 5, 1401, 440-449. doi: 10.30476/ijms.2022.90791.2178
Gholamiankhah, Faeze, Mostafapour, Samaneh, Abdi Goushbolagh, Nouraddin, Shojaerazavi, Seyedjafar, Layegh, Parvaneh, Tabatabaei, Seyyed Mohammad, Arabi, Hossein. (1401). 'Automated Lung Segmentation from Computed Tomography Images of Normal and COVID-19 Pneumonia Patients', سامانه مدیریت نشریات علمی, 47(5), pp. 440-449. doi: 10.30476/ijms.2022.90791.2178
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. سامانه مدیریت نشریات علمی, 1401; 47(5): 440-449. doi: 10.30476/ijms.2022.90791.2178

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
سایر فایل های مرتبط با مقاله
مراجع
  1. Shirani K, Sheikhbahaei E, Torkpour Z, Ghadiri Nejad M, Kamyab Moghadas B, Ghasemi M, et al. A Narrative Review of COVID-19: The New Pandemic Disease. Iran J Med Sci. 2020;45:233-49. doi: 10.30476/ijms.2020.85869.1549. PubMed PMID: 32801413; PubMed Central PMCID: PMCPMC7395956.
  2. Pourafkari L, Mirza-Aghzadeh-Attari M, Zarrintan A, Mousavi-Aghdas SA. Clinical Experience, Pathophysiology, and Considerations in the Prophylaxis and Treatment of Hypercoagulopathy of COVID-19: A Review Study. Iran J Med Sci. 2021;46:1-14. doi: 10.30476/ijms.2020.87233.1730. PubMed PMID: 33487787; PubMed Central PMCID: PMCPMC7812501.
  3. Zhou L, Li Z, Zhou J, Li H, Chen Y, Huang Y, et al. A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis. IEEE Trans Med Imaging. 2020;39:2638-52. doi: 10.1109/TMI.2020.3001810. PubMed PMID: 32730214; PubMed Central PMCID: PMCPMC8769013.
  4. Yan Q, Wang B, Gong D, Luo C, Zhao W, Shen J, et al. COVID-19 chest CT image segmentation network by multi-scale fusion and enhancement operations. IEEE Transactions on Big Data. 2021;7:13-24. doi: 10.1109/TBDATA.2021.3056564.
  5. Fan DP, Zhou T, Ji GP, Zhou Y, Chen G, Fu H, et al. Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images. IEEE Trans Med Imaging. 2020;39:2626-37. doi: 10.1109/TMI.2020.2996645. PubMed PMID: 32730213.
  6. Minaee S, Kafieh R, Sonka M, Yazdani S, Jamalipour Soufi G. Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning. Med Image Anal. 2020;65:101794. doi: 10.1016/j.media.2020.101794. PubMed PMID: 32781377; PubMed Central PMCID: PMCPMC7372265.
  7. Arabi H, Asl ARK, editors. Feasibility study of a new approach for reducing of partial volume averaging artifact in CT scanner. Isfahan: 17th Iranian Conference of Biomedical Engineering (ICBME); 2010. doi: 10.1109/ICBME.2010.5704968.
  8. Lohrabian V, Kamali-Asl A, Harvani HG, Aghdam SRH, Arabi H, Zaidi H. Comparison of the X-ray tube spectrum measurement using BGO, NaI, LYSO, and HPGe detectors in a preclinical mini-CT scanner: Monte Carlo simulation and practical experiment. Radiation Physics and Chemistry. 2021;189:109666. doi: 10.1016/j.radphyschem.2021.109666.
  9. Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, et al. Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases. Radiology. 2020;296:E32-E40. doi: 10.1148/radiol.2020200642. PubMed PMID: 32101510; PubMed Central PMCID: PMCPMC7233399.
  10. Zheng C, Deng X, Fu Q, Zhou Q, Feng J, Ma H, et al. Deep learning-based detection for COVID-19 from chest CT using weak label. MedRxiv. 2020. doi: 10.1101/2020.03.12.20027185.
  11. Zhang K, Liu X, Shen J, Li Z, Sang Y, Wu X, et al. Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography. Cell. 2020;181:1423-33. doi: 10.1016/j.cell.2020.04.045. PubMed PMID: 32416069; PubMed Central PMCID: PMCPMC7196900.
  12. Lessmann N, Sanchez CI, Beenen L, Boulogne LH, Brink M, Calli E, et al. Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence. Radiology. 2021;298:E18-E28. doi: 10.1148/radiol.2020202439. PubMed PMID: 32729810; PubMed Central PMCID: PMCPMC7393955.
  13. Hofmanninger J, Prayer F, Pan J, Rohrich S, Prosch H, Langs G. Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem. Eur Radiol Exp. 2020;4:50. doi: 10.1186/s41747-020-00173-2. PubMed PMID: 32814998; PubMed Central PMCID: PMCPMC7438418.
  14. Arabi H, Zaidi H. Comparison of atlas-based techniques for whole-body bone segmentation. Med Image Anal. 2017;36:98-112. doi: 10.1016/j.media.2016.11.003. PubMed PMID: 27871000.
  15. Gerard SE, Herrmann J, Xin Y, Martin KT, Rezoagli E, Ippolito D, et al. CT image segmentation for inflamed and fibrotic lungs using a multi-resolution convolutional neural network. Sci Rep. 2021;11:1455. doi: 10.1038/s41598-020-80936-4. PubMed PMID: 33446781; PubMed Central PMCID: PMCPMC7809065.
  16. Skourt BA, El Hassani A, Majda A. Lung CT image segmentation using deep neural networks. Procedia Computer Science. 2018;127:109-13. doi: 10.1016/j.procs.2018.01.104.
  17. Arabi H, Zaidi H. Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy. Eur J Hybrid Imaging. 2020;4:17. doi: 10.1186/s41824-020-00086-8. PubMed PMID: 34191161; PubMed Central PMCID: PMCPMC8218135.
  18. Zhou T, Canu S, Ruan S. Automatic COVID-19 CT segmentation using U-Net integrated spatial and channel attention mechanism. Int J Imaging Syst Technol. 2021;31:16-27. doi: 10.1002/ima.22527. PubMed PMID: 33362345; PubMed Central PMCID: PMCPMC7753491.
  19. Bahrami A, Karimian A, Fatemizadeh E, Arabi H, Zaidi H. A new deep convolutional neural network design with efficient learning capability: Application to CT image synthesis from MRI. Med Phys. 2020;47:5158-71. doi: 10.1002/mp.14418. PubMed PMID: 32730661.
  20. Karimi D, Dou H, Warfield SK, Gholipour A. Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis. Med Image Anal. 2020;65:101759. doi: 10.1016/j.media.2020.101759. PubMed PMID: 32623277; PubMed Central PMCID: PMCPMC7484266.
  21. Arabi H, Zaidi H. Magnetic resonance imaging-guided attenuation correction in whole-body PET/MRI using a sorted atlas approach. Med Image Anal. 2016;31:1-15. doi: 10.1016/j.media.2016.02.002. PubMed PMID: 26948109.
  22. Yao Q, Xiao L, Liu P, Zhou SK. Label-Free Segmentation of COVID-19 Lesions in Lung CT. IEEE Trans Med Imaging. 2021;40:2808-19. doi: 10.1109/TMI.2021.3066161. PubMed PMID: 33760731.
  23. Wang X, Deng X, Fu Q, Zhou Q, Feng J, Ma H, et al. A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT. IEEE Trans Med Imaging. 2020;39:2615-25. doi: 10.1109/TMI.2020.2995965. PubMed PMID: 33156775.
  24. Zhao S, Li Z, Chen Y, Zhao W, Xie X, Liu J, et al. SCOAT-Net: A novel network for segmenting COVID-19 lung opacification from CT images. Pattern Recognit. 2021;119:108109. doi: 10.1016/j.patcog.2021.108109. PubMed PMID: 34127870; PubMed Central PMCID: PMCPMC8189738.
  25. Oulefki A, Agaian S, Trongtirakul T, Kassah Laouar A. Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images. Pattern Recognit. 2021;114:107747. doi: 10.1016/j.patcog.2020.107747. PubMed PMID: 33162612; PubMed Central PMCID: PMCPMC7605758.
  26. Alnaser A, Gong B, Moeller K. Evaluation of open-source software for the lung segmentation. Current Directions in Biomedical Engineering. 2016;2:515-8. doi: 10.1515/cdbme-2016-0114.
  27. Gibson E, Li W, Sudre C, Fidon L, Shakir DI, Wang G, et al. NiftyNet: a deep-learning platform for medical imaging. Comput Methods Programs Biomed. 2018;158:113-22. doi: 10.1016/j.cmpb.2018.01.025. PubMed PMID: 29544777; PubMed Central PMCID: PMCPMC5869052.
  28. Arabi H, Zaidi H. Deep learning-guided estimation of attenuation correction factors from time-of-flight PET emission data. Med Image Anal. 2020;64:101718. doi: 10.1016/j.media.2020.101718. PubMed PMID: 32492585.
  29. Li W, Wang G, Fidon L, Ourselin S, Cardoso MJ, Vercauteren T. On the compactness, efficiency, and representation of 3D convolutional networks: brain parcellation as a pretext task. International Conference on Information Processing in Medical Imaging. 2017:348-60. doi: 10.1007/978-3-319-59050-9_28.
  30. Rubin GD, Ryerson CJ, Haramati LB, Sverzellati N, Kanne JP, Raoof S, et al. The Role of Chest Imaging in Patient Management During the COVID-19 Pandemic: A Multinational Consensus Statement From the Fleischner Society. Chest. 2020;158:106-16. doi: 10.1016/j.chest.2020.04.003. PubMed PMID: 32275978; PubMed Central PMCID: PMCPMC7138384.
  31. Fang Y, Zhang H, Xie J, Lin M, Ying L, Pang P, et al. Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR. Radiology. 2020;296:E115-E7. doi: 10.1148/radiol.2020200432. PubMed PMID: 32073353; PubMed Central PMCID: PMCPMC7233365.
  32. Ma J, Wang Y, An X, Ge C, Yu Z, Chen J, et al. Toward data-efficient learning: A benchmark for COVID-19 CT lung and infection segmentation. Med Phys. 2021;48:1197-210. doi: 10.1002/mp.14676. PubMed PMID: 33354790.
  33. Shi F, Wang J, Shi J, Wu Z, Wang Q, Tang Z, et al. Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19. IEEE Rev Biomed Eng. 2021;14:4-15. doi: 10.1109/RBME.2020.2987975. PubMed PMID: 32305937.
  34. Tilborghs S, Dirks I, Fidon L, Willems S, Eelbode T, Bertels J, et al. Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients. arXiv preprint arXiv:200715546. 2020. 
  35. Loey M, Smarandache F, M. Khalifa NE. Within the lack of chest COVID-19 X-ray dataset: a novel detection model based on GAN and deep transfer learning. Symmetry. 2020;12:651. doi: 10.3390/sym12040651.
  36. Trivizakis E, Tsiknakis N, Vassalou EE, Papadakis GZ, Spandidos DA, Sarigiannis D, et al. Advancing COVID-19 differentiation with a robust preprocessing and integration of multi-institutional open-repository computer tomography datasets for deep learning analysis. Exp Ther Med. 2020;20:78. doi: 10.3892/etm.2020.9210. PubMed PMID: 32968435; PubMed Central PMCID: PMCPMC7500043.
  37. Muller D, Soto-Rey I, Kramer F. Robust chest CT image segmentation of COVID-19 lung infection based on limited data. Inform Med Unlocked. 2021;25:100681. doi: 10.1016/j.imu.2021.100681. PubMed PMID: 34337140; PubMed Central PMCID: PMCPMC8313817.
  38. Suri JS, Agarwal S, Pathak R, Ketireddy V, Columbu M, Saba L, et al. COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models. Diagnostics (Basel). 2021;11. doi: 10.3390/diagnostics11081405. PubMed PMID: 34441340; PubMed Central PMCID: PMCPMC8392426.
  39. Zhao C, Xu Y, He Z, Tang J, Zhang Y, Han J, et al. Lung segmentation and automatic detection of COVID-19 using radiomic features from chest CT images. Pattern Recognit. 2021;119:108071. doi: 10.1016/j.patcog.2021.108071. PubMed PMID: 34092815; PubMed Central PMCID: PMCPMC8169223.
  40. Tank VH, Ghosh R, Gupta V, Sheth N, Gordon S, He W, et al. Drug eluting stents versus bare metal stents for the treatment of extracranial vertebral artery disease: a meta-analysis. J Neurointerv Surg. 2016;8:770-4. doi: 10.1136/neurintsurg-2015-011697. PubMed PMID: 26180094.
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