ECGANCOVID: Efficient Conditional GAN Architecture for Covid-19 Disease Segmentation
DOI:
https://doi.org/10.21123/bsj.2024.9335Keywords:
COVID-19 disease, Computed tomography (CT) images, Conditional generative adversarial network (CGAN), Lung and lesion segmentation, Hierarchical segmentation strategyAbstract
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) poses a global threat, impacting millions worldwide. While automated detection of lung infections through Computed Tomography (CT) scans is a promising alternative, segmenting infected regions from CT slices remains challenging due to low-contrast infection boundaries and blurred appearances. To address this challenge, A deep-learning model called ECGANCOVID-Net is proposed for detection and identification of infected regions in chest CT images. Our model utilizes a semantic hierarchical segmenter to detect regions of lung infection caused by Coronavirus in CT medical images. The model consists of two components, namely the U-CGAN-Net models. The initial neural network, UCGAN-Net1, is designed to detect lung parenchyma. Subsequently, the second neural network, UCGAN-Net 2, operates on the segmented lungs to accurately identify the specific regions impacted by COVID-19 lesions. UCGAN-Net comprises a conditional generative adversarial network (CGAN) incorporating an adapted generator and discriminator.Furthermore, our model employs data augmentation techniques to address the issue of limited training data.Through extensive trials, it has been discovered that the suggested methodology exhibits superior performance compared to recently proposed techniques . This is particularly evident in the improved overall performance of our model when accurately determining the location of tiny lesions. The proposed ECGANCOVID net has demonstrated exceptional performance in segmenting COVID-19 lesions, achieving higher localization performance with a Dice Similarity Coefficient (DSC) of 84.5% and Intersection over Union (IOU). Additionally, the suggested model has undergone external validation using an unseen dataset, resulting in Dice Similarity Coefficient of 69.7%.
Received 09/09/2023
Revised 07/01/2024
Accepted 09/01/2024
Published Online First 20/09/2024
References
Kakodkar P, Kaka N, Baig M. A Comprehensive Literature Review on the Clinical Presentation and Management of the Pandemic Coronavirus Disease 2019 (COVID-19). Cureus. 2020; 18. https://doi.org/10.7759/cureus.7560
Muller D, Soto-rey I, Kramer F.Automated Chest CT Image Segmentation of COVID-19 Lung Infection based on 3D U-Net. arXiv preprint arXiv:2007.04774.2020.
Mansoor A, Bagci U, Foster B, Xu Z, Papadakis GZ, Folio LR, et al. Segmentation and image analysis of abnormal lungs at CT: Current approaches, challenges, and future trends. Radiographics. 2015; 35(4): 1056–1076. https://doi.org/10.1148%2Frg.2015140232
Kass M, Witkin A, Terzopoulos D. Snakes: Active contour models. Int J Comput Vis. 1988; 1(4): 321–31. https://doi.org/10.1007/BF00133570
Malladi R, Sethian JA, Vemuri BC. Shape Modeling with Front Propagation: A Level Set Approach. IEEE Trans Pattern Anal Mach Intell. 1995; 17(2): 158-175 . https://doi.org/10.1109/34.368173
Beucher S, Lantuejoul C. Use of Watersheds in Contour Detection. International Workshop on Image Processing: Real-time Edge and Motion Detection/Estimation. 1979;(January 1979): 12–21.
Adams R, Bischof L. Seeded Region Growing. IEEE Trans Pattern Anal Mach Intell. 1994;16(6):641–7. https://doi.org/10.1109/34.295913
Manjunath BS, Chellappa R. Unsupervised Texture Segmentation Using Markov Random Field Models. IEEE Trans Pattern Anal Mach Intell. 1991; 13: 478–482. https://doi.org/10.1109/34.134046
Han C, Duan Y, Tao X, Lu J. Dense Convolutional Networks for Semantic Segmentation. IEEE Access. 2019; 7: 43369–82. https://doi.org/10.1109/ACCESS.2019.2908685
Weng W, Zhu X. INet: Convolutional Networks for Biomedical Image Segmentation. IEEE Access. 2021; 9: 16591–603. https://doi.org/10.1109/ACCESS.2021.3053408
Oktay O, Schlemper J, Folgoc L Le, Lee M, Heinrich M, Misawa K, et al. Attention U-Net: Learning Where to Look for the Pancreas. arXivpreprint arXiv:1804.03999. 2018; https://doi.org/10.48550/arXiv.1804.03999
Md Zahangir Alom, Chris Yakopcic, Tarek M Taha, Vijayan K. Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation. arXiv preprint arXiv:1802.06955. 2018; https://doi.org/10.48550/arXiv.1802.06955
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial networks. Commun ACM. 2020; 63(11):7. https://doi.org/10.1145/3422622
Mirza M, Osindero S. Conditional Generative Adversarial Nets. arXiv preprint arXiv:1411.1784. 2014. https://doi.org/10.48550/arXiv.1411.1784
Salman AO, Geman O. Evaluating Three Machine Learning Classification Methods for Effective COVID-19 Diagnosis. Int J Math Comput Sci . . 2023 Jan 15; 1: 1–14. http://dx.doi.org/10.59543/ijmscs.v1i.7693
Arif ZH, Cengiz K. Severity Classification for COVID-19 Infections based on Lasso-Logistic Regression Model. Int J Math Comput Sci .2023; 1: 25–32. https://dx.doi.org/10.59543/ijmscs.v1i.7715
Abdulkareem KH, Al-Mhiqani MN, Dinar AM, Mohammed MA, Al-Imari MJ, Al-Waisy AS, et al. MEF: Multidimensional Examination Framework for Prioritization of COVID-19 Severe Patients and Promote Precision Medicine Based on Hybrid Multi-Criteria Decision-Making Approaches. Bioengineering. 2022; 9(9): 457-489 . https://doi.org/10.3390/bioengineering9090457
Li Y, Wei D, Chen J, Cao S, Zhou H, Zhu Y, et al. Efficient and Effective Training of COVID-19 Classification Networks with Self-Supervised Dual-Track Learning to Rank. IEEE J Biomed Health Inform. 2020 ;24(10): 2787–2797 . https://doi.org/10.1109/JBHI.2020.3018181
Owais M, Baek NR, Park KR. Domain-adaptive artificial intelligence-based model for personalized diagnosis of trivial lesions related to COVID-19 in chest computed tomography scans. J Pers Med. 2021 Oct 1; 11(10). https://doi.org/10.3390/jpm11101008
Mondal MRH, Bharati S, Podder P. CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images. PLoS One. 2021; 16(10 October): 1–24. https://doi.org/10.1371/journal.pone.0259179
Hasan N, Bao Y, Shawon A, Huang Y. DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image. SN Comput Sci. 2021; 2(5): 1–11. https://doi.org/10.1007/s42979-021-00782-7
Zhang X, Lu S, Wang SH, Yu X, Wang SJ, Yao L, et al. Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning Architecture. J Comput Sci Technol. 2022; 37(2): 330–43. https://doi.org/10.1007/s11390-020-0679-8
Motwani A, Shukla PK, Pawar M, Kumar M, Ghosh U, Numay W Al, et al. Enhanced Framework for COVID-19 Prediction with Computed Tomography Scan Images using Dense Convolutional Neural Network and Novel Loss Function. Comput Electr Eng. 2022; 105: 108479. https://doi.org/10.1016/j.compeleceng.2022.108479
Podder P, Das SR, Mondal MRH, Bharati S, Maliha A, Hasan MJ, et al. LDDNet: A Deep Learning Framework for the Diagnosis of Infectious Lung Diseases. Sensors. 2023; 23(1). https://doi.org/10.3390/s23010480
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 Oct 1; 65. https://doi.org/10.1016/j.media.2020.101794
Mohammed MA, Al-Khateeb B, Yousif M, Mostafa SA, Kadry S, Abdulkareem KH, et al. Novel Crow Swarm Optimization Algorithm and Selection Approach for Optimal Deep Learning COVID-19 Diagnostic Model. Comput Intell Neurosci. 2022; 2022. https://doi.org/10.1155/2022/1307944
Long, Jonathan, Evan Shelhamer, and Trevor Darrell. Fully convolutional networks for semantic segmentation. Proc IEEE Conf Comput Vis Patt Rec. 2015; 3431-3440. https://doi.org/10.48550/arXiv.1411.4038
Ouyang X, Huo J, Xia L, Shan F, Liu J, Mo Z, et al. Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumonia. IEEE Trans Med Imaging . 2020; 39(8): 2595-2605 https://doi.org/10.48550/arXiv.2005.02690
Amyar A, Modzelewski R, Li H, Ruan S. Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation. Comput Biol Med. 2020; 126. https://doi.org/10.1016/j.compbiomed.2020.104037
Yan Q, Wang B, Gong D, Luo C, Zhao W, Shen J, et al. COVID-19 Chest CT Image Segmentation --A Deep Convolutional Neural Network Solution. arXiv preprint arXiv:2004.10987. 2020 ; https://doi.org/10.48550/arXiv.2004.10987
Hu S, Gao Y, Niu Z, Jiang Y, Li L, Xiao X, et al. Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Images. IEEE Access. 2020; 8: 118869–118883 . https://doi.org/10.48550/arXiv.2004.06689
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 Jun 1; 114. https://doi.org/10.1016/j.patcog.2020.107747
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 Apr 22; 39(8): 2626-2637. https://doi.org/10.1109/tmi.2020.2996645
Mu N, Wang H, Zhang Y, Jiang J, Tang J. Progressive global perception and local polishing network for lung infection segmentation of COVID-19 CT images. Pattern Recognit. 2021 Dec 1; 120. https://doi.org/10.1016/j.patcog.2021.108168
He J, Zhu Q, Zhang K, Yu P, Tang J. An evolvable adversarial network with gradient penalty for COVID-19 infection segmentation. Appl Soft Comput. 2021 Dec 1; 113. https://doi.org/10.1016/j.asoc.2021.107947
COVID-19 CT Lung and Infection Segmentation Dataset | Zenodo . 2020.
COVID-19 - Medical segmentation. 2020.
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. 2020; 48(3): 1197–210. https://doi.org/10.1002/mp.14676
Radiopaedia.org, the peer-reviewed collaborative radiology resource.. https://radiopaedia.org/
Coronacases.org – by Raioss.com [Internet]. [cited 2023 Jul 23] .
Pizer SM, Philip Amburn E, Austin JD, Cromartie R, Geselowitz A, Greer T, et al. Adaptive Histogram Equalization and Its Variations. Comput Vis Graph Image Process. 1987; 39(3): 355-368 . https://doi.org/10.1016/S0734-189X(87)80186-X
Zimmerman JB, Pizer SM, Staab E V, Perry JR, Mccartney W, Brenton BC. An Evaluation of the Effectiveness of Adaptive Histogram Equalization for Contrast Enhancement. IEEE Trans Med Imaging. 1988; 7(4): 304-312. https://doi.org/10.1109/42.14513
Pizer SM, Johnston RE, Ericksen JP, Yankaskas BC, Muller KE. Contrast-Limited Adaptive Histogram Equalization: Speed and Effectiveness. Proceedings of the first conf. on visual. in biomedical computing.1990. https://doi.org/10.1109/VBC.1990.109340
Khalifa NE, Loey M, Mirjalili S. A comprehensive survey of recent trends in deep learning for digital images augmentation. Artif Intell Rev . 2022; 55(3): 2351–77. https://doi.org/10.1007/s10462-021-10066-4
Isola P, Zhu JY, Zhou T, Efros AA. Image-to-image translation with conditional adversarial networks. Proc. - 30th IEEE Conf Comput Vis Patt Rec. 2017; 1125-1134. https://doi.org/10.48550/arXiv.1611.07004
Ruder S. An overview of gradient descent optimization algorithms. arXiv preprint arXiv: 1609.04747. 2016 ; https://doi.org/10.48550/arXiv.1609.04747
Punn NS, Agarwal S. CHS-Net: A Deep Learning Approach for Hierarchical Segmentation of COVID-19 via CT Images. Neural Process Lett. 2022; 54(5): 3771–92. https://doi.org/10.1007/s11063-022-10785-x
Qiu Y, Liu Y, Li S, Xu J. MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation. 35th AAAI Conference on Artificial Intelligence, AAAI 2021. 2021; 6A: 4846–54. https://doi.org/10.48550/arXiv.2004.09750
Zheng R, Zheng Y, Dong-Ye C. Improved 3D U-Net for COVID-19 Chest CT Image Segmentation. Sci Program. 2021; 2021. https://doi.org/10.1155/2021/9999368
Müller 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: 11 https://doi.org/10.1016/j.imu.2021.100681
Alirr OI. Automatic deep learning system for COVID-19 infection quantification in chest CT. Multimed Tools Appl . 2022; 81(1): 527–41. https://doi.org/10.1007/s11042-021-11299-9
He T, Liu H, Zhang Z, Li C, Zhou Y. Research on the Application of Artificial Intelligence in Public Health Management: Leveraging Artificial Intelligence to Improve COVID-19 CT Image Diagnosis. Int J Environ Res Public Health. 2023; 20(2): 1158-1170. https://doi.org/10.3390/ijerph20021158
Zhang P, Zhong Y, Deng Y, Tang X, Li X. CoSinGAN: Learning COVID-19 infection segmentation from a single radiological image. Diagnostics. 2020;10(11):1–28. https://doi.org/10.3390/diagnostics10110901
Yang Q, Li Y, Zhang M, Wang T, Yan F, Xie C. Automatic Segmentation of COVID-19 CT Images using improved MultiResUNet. Proc 2020 Chinese Autom Congr. 2020; 1614-1618.. https://doi.org/10.1109/CAC51589.2020.9327668
Wang Y, Zhang Y, Liu Y, Tian J, Zhong C, Shi Z, et al. Does non-COVID-19 lung lesion help? investigating transferability in COVID-19 CT image segmentation. Comput Methods Programs Biomed . 2021; 202: 106004. https://doi.org/10.1016/j.cmpb.2021.106004
Zhao X, Zhang P, Song F, Fan G, Sun Y, Wang Y, et al. D2A U-Net: Automatic Segmentation of COVID-19 Lesions from CT Slices with Dilated Convolution and Dual Attention Mechanism. arXiv preprint arXiv:2102. 05210. 2021; 11 . https://doi.org/10.1016/j.compbiomed.2021.104526
Laradji I, Rodriguez P, Manas O, Lensink K, Law M, Kurzman L, et al. A weakly supervised consistency-based learning method for COVID-19 Segmentation in CT images. Proc 2021 IEEE Winter Conf Appl Comput Vis. 2021; 2452–2461. https://doi.org/10.48550/arXiv.2007.07012
Jia H, Tang H, Ma G, Cai W, Huang H, Zhan L, et al . PSGR: Pixel-wise Sparse Graph Reasoning for COVID-19 Pneumonia Segmentation in CT Images. arXiv preprint. arXiv:2108.03809. 2021; 1–10. https://doi.org/10.48550/arXiv.2108.03809
Singh VK, Abdel-Nasser M, Pandey N, Puig D. Lunginfseg: Segmenting covid-19 infected regions in lung ct images based on a receptive-field-aware deep learning framework. Diagnostics. 2021; 11(2): 158-177 https://doi.org/10.3390/diagnostics11020158
Chen Y, Zhou T, Chen Y, Feng L, Zheng C, Liu L, et al. HADCNet: Automatic segmentation of COVID-19 infection based on a hybrid attention dense connected network with dilated convolution. Comput Biol Med . 2022; 149(July): 105981. https://doi.org/10.1016/j.compbiomed.2022.105981
Wang X, Yuan Y, Guo D, Huang X, Cui Y, Xia M, et al. SSA-Net: Spatial self-attention network for COVID-19 pneumonia infection segmentation with semi-supervised few-shot learning. Med Image Anal .2022; 79: 102459. https://doi.org/10.1016/j.media.2022.102459
Owais M, Baek NR, Park KR. DMDF-Net: Dual multiscale dilated fusion network for accurate segmentation of lesions related to COVID-19 in lung radiographic scans. Expert Syst Appl. 2022 Sep 15;202. https://doi.org/10.1016/j.eswa.2022.117360
Jia H, Tang H, Ma G, Cai W, Huang H, Zhan L, et al. A convolutional neural network with pixel-wise sparse graph reasoning for COVID-19 lesion segmentation in CT images. Comput Biol Med . 2023; 155: 106698. https://doi.org/10.1016/j.compbiomed.2023.106698
Fan X, Feng X. SELDNet: Sequenced encoder and lightweight decoder network for COVID-19 infection region segmentation. Displays. 2023; 77(January): 102395. https://doi.org/10.1016/j.displa.2023.102395
Peng Y, Zhang T, Guo Y. Cov-TransNet: Dual branch fusion network with transformer for COVID-19 infection segmentation. Biomed Signal Process Control. 2023; 80. https://doi.org/10.1016/j.bspc.2022.104366
Downloads
Issue
Section
License
Copyright (c) 2024 Payman Hussein Hussan, Israa Hadi Ali
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
