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GIFNet: an effective global infection feature network for automatic COVID-19 lung lesions segmentation

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Abstract

The ongoing COronaVIrus Disease 2019 (COVID-19) pandemic carried by the SARS-CoV-2 virus spread worldwide in early 2019, bringing about an existential health catastrophe. Automatic segmentation of infected lungs from COVID-19 X-ray and computer tomography (CT) images helps to generate a quantitative approach for treatment and diagnosis. The multi-class information about the infected lung is often obtained from the patient’s CT dataset. However, the main challenge is the extensive range of infected features and lack of contrast between infected and normal areas. To resolve these issues, a novel Global Infection Feature Network (GIFNet)-based Unet with ResNet50 model is proposed for segmenting the locations of COVID-19 lung infections. The Unet layers have been used to extract the features from input images and select the region of interest (ROI) by using the ResNet50 technique for training it faster. Moreover, integrating the pooling layer into the atrous spatial pyramid pooling (ASPP) mechanism in the bottleneck helps for better feature selection and handles scale variation during training. Furthermore, the partial differential equation (PDE) approach is used to enhance the image quality and intensity value for particular ROI boundary edges in the COVID-19 images. The proposed scheme has been validated on two datasets, namely the SARS-CoV-2 CT scan and COVIDx-19, for detecting infected lung segmentation (ILS). The experimental findings have been subjected to a comprehensive analysis using various evaluation metrics, including accuracy (ACC), area under curve (AUC), recall (REC), specificity (SPE), dice similarity coefficient (DSC), mean absolute error (MAE), precision (PRE), and mean squared error (MSE) to ensure rigorous validation. The results demonstrate the superior performance of the proposed system compared to the state-of-the-art (SOTA) segmentation models on both X-ray and CT datasets.

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Data availability

The data that support the finding made in this manuscript can be accessed via the following link: https://github.com/HzFu/COVID19_imaging_AI_paper_list for SARS-CoV-2 CT scan dataset and link: https://www.kaggle.com/datasets/prashant268/chest-xray-COVID19-pneumonia for COVIDx-19 dataset.

References

  1. Johns Hopkins University (2021) COVID-19 dashboard. [Online]. Available: https://coronavirus.jhu.edu/map.html Accessed Jun 10 2023

  2. Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, Xia L (2020) Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology 296(2):32–40. https://doi.org/10.1148/radiol.2020200642

    Article  CAS  Google Scholar 

  3. Lyu F, Ye M, Carlsen JF, Erleben K, Darkner S, Yuen PC (2022) Pseudo-label guided image synthesis for semi-supervised COVID-19 pneumonia infection segmentation. IEEE Trans Med Imaging 42(3):797–809. https://doi.org/10.1109/TMI.2022.3217501

    Article  Google Scholar 

  4. Murmu A, Kumar P (2021) Deep learning model-based segmentation of medical diseases from MRI and CT images. In: TENCON 2021 IEEE Region 10 Conference (TENCON): pp 608–613. https://doi.org/10.1109/TENCON54134.2021.9707278

  5. Sailunaz K, Özyer T, Rokne J, Alhajj R, (2023) A survey of machine learning-based methods for COVID-19 medical image analysis. Med Biol Eng Compu 1–41. https://doi.org/10.1007/s11517-022-02758-y

  6. Yao Q, Xiao L, Liu P, Zhou SK (2021) Label-free segmentation of COVID-19 lesions in lung CT. IEEE Trans Med Imaging 40(10):2808–2819. https://doi.org/10.1109/TMI.2021.3066161

    Article  PubMed  Google Scholar 

  7. Murmu A, Kumar P (2023) A novel gateaux derivatives with efficient DCNN-ResUNet method for segmenting multi-class brain tumor. Med Biol Eng Compu. https://doi.org/10.1007/s11517-023-02824-z. (In press)

  8. Zhang J, Chen D, Ma D, Ying C, Sun X, Xu X, Cheng Y (2023) CdcSegNet: automatic COVID-19 infection segmentation from CT images. IEEE Trans Instrum Meas 72. https://doi.org/10.1109/TIM.2023.3267355

  9. Cobes N, Guernou M, Lussato D, Queneau M, Songy B, Bonardel G, Grellier JF (2020) Ventilation/perfusion SPECT/CT findings in different lung lesions associated with COVID-19: a case series. Eur J Nucl Med Mol Imaging 47(10):2453–2460. https://doi.org/10.1007/s00259-020-04920-w

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Abdel-Basset M, Chang V, Mohamed R (2020) HSMA_WOA: a hybrid novel Slime mould algorithm with whale optimization algorithm for tackling the image segmentation problem of chest X-ray images. Appl Soft Comput 95:106642. https://doi.org/10.1016/j.asoc.2020.106642

    Article  PubMed  PubMed Central  Google Scholar 

  11. Fan DP, Zhou T, Ji GP, Zhou Y, Chen G, Fu H, Shao L (2020) Inf-Net: automatic COVID-19 lung infection segmentation from CT images. IEEE Trans Med Imaging 39(8):2626–2637. https://doi.org/10.1109/TMI.2020.2996645

    Article  PubMed  Google Scholar 

  12. Qiu Y, Liu Y, Li S, Xu J (2021) MiniSeg: an extremely minimum network for efficient COVID-19 segmentation. Proceed AAAI Conf Artificial Intell 35(6):4846–4854. https://doi.org/10.1609/aaai.v35i6.16617

    Article  Google Scholar 

  13. Yan Q, Wang B, Gong D, Luo C, Zhao W, Shen J, You Z (2020) COVID-19 chest CT image segmentation–a deep convolutional neural network solution. arXiv:2004.10987

  14. Saeedizadeh N, Minaee S, Kafieh R, Yazdani S, Sonka M (2021) COVID TV-Unet: segmenting COVID-19 chest CT images using connectivity imposed Unet. Comput method Prog in Biomed Update 1:100007. https://doi.org/10.1016/j.cmpbup.2021.100007

    Article  Google Scholar 

  15. Fan C, Zeng Z, Xiao L, Qu X (2022) GFNet: automatic segmentation of COVID-19 lung infection regions using CT images based on boundary features. Pattern Recogn 132:108963. https://doi.org/10.1016/j.patcog.2022.108963

    Article  Google Scholar 

  16. Bougourzi F, Distante C, Dornaika F, Taleb-Ahmed A (2023) PDAtt-Unet: pyramid dual-decoder attention Unet for COVID-19 infection segmentation from CT-scans. Med Image Anal 86:102797. https://doi.org/10.1016/j.media.2023.102797

    Article  PubMed  PubMed Central  Google Scholar 

  17. Roth HR, Xu Z, Tor-Díez C, Jacob RS, Zember J, Molto J, Linguraru MG (2022) Rapid artificial intelligence solutions in a pandemic-the COVID-19-20 lung CT lesion segmentation challenge. Med Image Anal 82:102605. https://doi.org/10.1016/j.media.2022.102605

    Article  PubMed  PubMed Central  Google Scholar 

  18. Zhang Y, Liao Q, Yuan L, Zhu H, Xing J, Zhang J (2021) Exploiting shared knowledge from non-COVID lesions for annotation-efficient COVID-19 CT lung infection segmentation. IEEE J Biomed Health Inform 25(11):4152–4162. https://doi.org/10.1109/JBHI.2021.3106341

    Article  PubMed  Google Scholar 

  19. Wang G, Liu X, Li C, Xu Z, Ruan J, Zhu H, Zhang S (2020) A noise-robust framework for automatic segmentation of COVID-19 pneumonia lesions from CT images. IEEE Trans Med Imaging 39(8):2653–2663. https://doi.org/10.1109/TMI.2020.3000314

    Article  PubMed  Google Scholar 

  20. Liu J, Dong B, Wang S, Cui H, Fan DP, Ma J, Chen G (2021) COVID-19 lung infection segmentation with a novel two-stage cross-domain transfer learning framework. Med Image Anal 74:102205. https://doi.org/10.1016/j.media.2021.102205

    Article  PubMed  PubMed Central  Google Scholar 

  21. Roy K, Banik D, Bhattacharjee D, Krejcar O, Kollmann C (2022) LwMLA-NET: a lightweight multi-level attention-based network for segmentation of COVID-19 lungs abnormalities from CT images. IEEE Trans Instrum Meas 71:1–13. https://doi.org/10.1109/TIM.2022.3161690

    Article  Google Scholar 

  22. Chen H, Jiang Y, Ko H, Loew M (2023) A teacher-student framework with Fourier Transform augmentation for COVID-19 infection segmentation in CT images. Biomed Signal Process Control 79:104250. https://doi.org/10.1016/j.bspc.2022.104250

    Article  PubMed  Google Scholar 

  23. Rao Y, Lv Q, Zeng S, Yi Y, Huang C, Gao Y, Sun J (2023) COVID-19 CT ground-glass opacity segmentation based on attention mechanism threshold. Biomed Signal Process Control 81:104486. https://doi.org/10.1016/j.bspc.2022.104486

    Article  PubMed  Google Scholar 

  24. Khan A, Garner R, Rocca ML, Salehi S, Duncan D (2023) A novel threshold-based segmentation method for quantification of COVID-19 lung abnormalities. SIViP 17(4):907–914. https://doi.org/10.1007/s11760-022-02183-6

    Article  PubMed  Google Scholar 

  25. Fu H, Fan D-P, Chen G, Zhou T (2020) COVID-19 imaging-based AI research collection. Accessed 27 May 2020. [Online]. [Available online: https://github.com/HzFu/COVID19_imaging_AI_paper_list] [Accessed 15 Jun 2023

  26. COVID-19 radiography database [Available online: https://www.kaggle.com/datasets/prashant268/chest-xray-COVID19-pneumonia] Accessed Jun 15 2023

  27. Kumar P, Agrawal A (2015) Hardware accelerated multi-coordinate viewing framework for volumetric visualization of large 3D medical dataset. Procedia Comput Sci 54:566–573. https://doi.org/10.1016/j.procs.2015.06.065

    Article  Google Scholar 

  28. Demirel H, Anbarjafari G (2011) Discrete wavelet transform-based satellite image resolution enhancement. IEEE Trans Geosci Remote Sens 49(6):1997–2004. https://doi.org/10.1109/TGRS.2010.2100401

    Article  ADS  Google Scholar 

  29. Woo S, Park J, Lee JY, Kweon IS (2018) Cbam: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV). pp 3-19

  30. Saeedizadeh N, Minaee S, Kafieh R, Yazdani S, Sonka M (2021) COVID TV-Unet: segmenting COVID-19 chest CT images using connectivity imposed Unet. Comput Method Prog Biomed Update 1:100007. https://doi.org/10.1016/j.cmpbup.2021.100007

  31. Cong R, Yang H, Jiang Q, Gao W, Li H, Wang C, Kwong S (2022) BCS-Net: boundary, context, and semantic for automatic COVID-19 lung infection segmentation from CT images. IEEE Trans Instrum Meas 71:1–11. https://doi.org/10.1109/TIM.2022.3196430

    Article  Google Scholar 

  32. Cong R, Zhang Y, Yang N, Li H, Zhang X, Li R, Kwong S (2022) Boundary guided semantic learning for real-time COVID-19 lung infection segmentation system. IEEE Trans Consum Electron 68(4):376–386. https://doi.org/10.1109/TCE.2022.3205376

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Science and Engineering Department, National Institute of Technology Patna, Ashok Rajpath, Patna, Bihar, 800005, India

    Anita Murmu & Piyush Kumar

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  1. Anita Murmu
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Contributions

Anita Murmu (AnM) designed the environmental/implementation details platform, performed statistical analyses, and conducted experiments. Piyush Kumar (PiK) authored the literature survey and abstract section. AnM and PiK collaborated on writing the initial draft of the manuscript. PiK edited the first draft of the paper, while both authors contributed to analysis and result framing. Both authors reviewed and approved the final version of the manuscript. It is important to note that both Anita Murmu and Piyush Kumar made equal and significant contributions to this work.

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Correspondence to Anita Murmu.

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Murmu, A., Kumar, P. GIFNet: an effective global infection feature network for automatic COVID-19 lung lesions segmentation. Med Biol Eng Comput (2024). https://doi.org/10.1007/s11517-024-03024-z

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