Skip to main content
SpringerLink
Log in

CovLIS-MUnet segmentation model for Covid-19 lung infection regions in CT images

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

One of the most serious health concerns facing the world is coronavirus disease (COVID-19). COVID-19 is a virus that is highly infectious and contagious. The RT-PCR test is not the only way to diagnose COVID-19; many other alternatives are available like Lung Computed Tomography (CT) imaging. Large variances in texture, size, and location of infections make manual segmentation of lung CT images time-consuming and difficult. We present an effective segmentation model Covid-19 Lung Infection Segmentation based on a multi-special block Unet (CovLIS-MUnet) to improve the segmentation process. The primary goal of our study is to segment the lung and infection parts of CT scan images. We integrate a multi-special block (MSB) with a Convolutional block in the encoder and bridge phases, which helps to study contextual information and COVID-19 infection-related characteristics, thereby resulting in accurate segmentation results and improved prediction accuracy. The proposed method has been evaluated on a COVID-19 CT segmentation dataset. The findings of the qualitative experiments suggest that the CovLIS-MUnet model can accurately segment lung and COVID 19 affected areas, with accuracy 0.9964 and 0.998. The proposed CovLIS-MUnet consistently achieves much better segmentation performance across four widely used evaluation parameters, according to experimental data. The proposed model is good as compared to other existing models. Medical professionals will benefit greatly from the usage of CovLIS-MUnet segmentation architecture because, in addition to aiding in the diagnosis of COVID-19, it allows them to determine how serious the illness is through infection projections.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Algorithm
Fig. 2
Fig. 3
Figure 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data availability

Data are available at https://medicalsegmentation.com/covid19/.

References

  1. Zhu N et al (2020) A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med 382(8):727–733. https://doi.org/10.1056/nejmoa2001017

    Article  PubMed  PubMed Central  Google Scholar 

  2. Fang JXY, Zhang H et al (2020) Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology 395(3):1–2

    Google Scholar 

  3. Strunk JL, Temesgen H, Andersen H, Packalen P (2020) Correlation of chest CTand RTPCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology 80(2):1–8. https://doi.org/10.14358/PERS.80.2.000

    Article  Google Scholar 

  4. Munster VJ, Koopmans M, van Doremalen N, van Riel D, de Wit E (2020) A novel coronavirus emerging in China—key questions for impact assessment. N Engl J Med 382(8):692–694. https://doi.org/10.1056/nejmp2000929

    Article  PubMed  Google Scholar 

  5. Ng MY et al (2020) Imaging profile of the covid-19 infection: radiologic findings and literature review. Radiol Cardiothorac Imaging. https://doi.org/10.1148/ryct.2020200034

    Article  PubMed  PubMed Central  Google Scholar 

  6. Chung M et al (2020) CT imaging features of 2019 novel coronavirus (2019-NCoV). Radiology 295(1):202–207. https://doi.org/10.1148/radiol.2020200230

    Article  PubMed  Google Scholar 

  7. Gaál G, Maga B, Lukács A (2020) Attention U-net based adversarial architectures for chest X-ray lung segmentation. CEUR Workshop Proc 2692:1–7

    Google Scholar 

  8. Aswathy AL, Vinod Chandra SS (2022) Cascaded 3D UNet architecture for segmenting the COVID-19 infection from lung CT volume. Sci Rep 12(1):1–12. https://doi.org/10.1038/s41598-022-06931-z

    Article  CAS  Google Scholar 

  9. Zheng R, Zheng Y, Dong-Ye C (2021) Improved 3D U-Net for COVID-19 Chest CT Image Segmentation. Sci Program. https://doi.org/10.1155/2021/9999368

    Article  Google Scholar 

  10. A. Voulodimos, E. Protopapadakis, I. Katsamenis, A. Doulamis, and N. Doulamis (2021) “Deep learning models for COVID-19 infected area segmentation in CT images,” In: ACM International Conference Proceeding Series, pp. 404–411, doi: https://doi.org/10.1145/3453892.3461322.

  11. Zheng C et al (2020) Deep learning-based detection for COVID-19 from chest CT using weak label. IEEE Trans Med Imaging. https://doi.org/10.1101/2020.03.12.20027185

    Article  PubMed  Google Scholar 

  12. 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 Methods Programs Biomed Update 1:100007. https://doi.org/10.1016/j.cmpbup.2021.100007

    Article  PubMed  PubMed Central  Google Scholar 

  13. Minaee S, Abdolrashidi A, Su H, Bennamoun M, Zhang D (2023) Biometrics recognition using deep learning: a survey. Artif Intell Rev 56:1–49

    Article  Google Scholar 

  14. Badrinarayanan V, Kendall A, Cipolla R (2017) SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495. https://doi.org/10.1109/TPAMI.2016.2644615

    Article  PubMed  Google Scholar 

  15. Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H. (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV) (pp. 801-818). doi: https://doi.org/10.1007/978-3-030-01234-2_49.

  16. S. P. Mary, Ankayarkanni, U. Nandini, Sathyabama, and S. Aravindhan (2020) “A Survey on Image Segmentation Using Deep Learning,” J Phys Conf Ser, doi https://doi.org/10.1088/1742-6596/1712/1/012016.

  17. Z. Huang, X. Wang, L. Huang, C. Huang, Y. Wei, and W. Liu, (2019) “CCNet: Criss-cross attention for semantic segmentation,” In: Proceedings of the IEEE International Conference on Computer Vision, vol. 2019-Octob, pp. 603–612, 2019, doi: https://doi.org/10.1109/ICCV.2019.00069.

  18. Olaf Ronneberger TB (2015) Philipp Fischer and computer, “U-Net: convolutional networks for biomedical image segmentation.” IEEE Access 9:16591–16603. https://doi.org/10.1109/ACCESS.2021.3053408

    Article  Google Scholar 

  19. Fan DP et al (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 

  20. Wang G et al (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 

  21. Q. Yan et al., (2020) “COVID-19 Chest CT image segmentation -- A Deep Convolutional Neural Network Solution,” pp. 1–10.

  22. Zhou T, Canu S, Ruan S (2021) Automatic COVID-19 CT segmentation using U-Net integrated spatial and channel attention mechanism. Int J Imaging Syst Technol 31(1):16–27. https://doi.org/10.1002/ima.22527

    Article  PubMed  Google Scholar 

  23. O. Elharrouss, N. Almaadeed, N. Subramanian, and S. Al-maadeed, (2020) “An encoder-decoder-based method for COVID-19 lung infection segmentation”.

  24. X. Chen, L. Yao, and Y. Zhang, (2020) “Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images,” vol. 14, no. 8, pp. 1–7

  25. Saood A, Hatem I (2021) COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet. BMC Med Imaging 21(1):1–10. https://doi.org/10.1186/s12880-020-00529-5

    Article  Google Scholar 

  26. K. He, X. Zhang, S. Ren, and J. Sun, (2016) “Deep residual learning for image recognition,” In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 770–778, doi: https://doi.org/10.1109/CVPR.2016.90.

  27. Wu YH et al (2021) JCS: an explainable COVID-19 diagnosis system by joint classification and segmentation. IEEE Trans Image Process 30:3113–3126. https://doi.org/10.1109/TIP.2021.3058783

    Article  ADS  PubMed  Google Scholar 

  28. Xu R, Wang C, Xu S, Meng W, Zhang X (2023) Dual-stream representation fusion learning for accurate medical image segmentation. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2023.106402

    Article  Google Scholar 

  29. R. , W. C. , X. S. , M. W. , Z. X. Xu, DC-Net: Dual Context Network for 2D Medical Image Segmentation. , 1st ed., vol. 12901. Springer Cham, 2021.

  30. Sadeghi F, Taheri M, Rastgarpour M, Sharifi A (2022) A novel sep-unet architecture of convolutional neural networks to improve dermoscopic image segmentation by training parameters reduction. Int J Artif Intell. https://doi.org/10.36079/lamintang.ijai-0902.405

    Article  Google Scholar 

  31. Rajaragavi R, Palanivel Rajan S (2022) Optimized U-net segmentation and hybrid res-net for brain tumor MRI images classification. Intell Autom Soft Comput. https://doi.org/10.32604/iasc.2022.021206

    Article  Google Scholar 

  32. Yousefi T, Aktaş Ö (2023) New hybrid segmentation algorithm: UNet-GOA. PeerJ Comput Sci. https://doi.org/10.7717/peerj-cs.1499

    Article  PubMed  PubMed Central  Google Scholar 

  33. Z. Tang et al., “Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT Images,” vol. 2019, pp. 1–18, 2020.

  34. Shi F, Xia L, Shan F, Song B, Wu D, Wei Y, Yuan H, Jiang H, He Y, Gao Y, Sui H (2021) Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification. Phys Med Biol 66(6):065031

    Article  CAS  PubMed  Google Scholar 

  35. Ye Z, Zhang Y, Wang Y, Huang Z, Song B (2020) Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review. Eur Radiol 30(8):4381–4389. https://doi.org/10.1007/s00330-020-06801-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Funding

The authors declare that no funds, grants, or other supports were received during the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Haryana, India

    Manju Devi & Sukhdip Singh

  2. Thapar Institute of Engineering and Technology (TIET), Patiala, Punjab, India

    Shailendra Tiwari

Authors
  1. Manju Devi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sukhdip Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shailendra Tiwari
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The authors contributed to each part of this paper equally. The authors read and approved the final manuscript.

Corresponding author

Correspondence to Manju Devi.

Ethics declarations

Conflict of interest

The authors have no conflicts of interest, financial or otherwise.

Ethical approval

Not applicable.

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Devi, M., Singh, S. & Tiwari, S. CovLIS-MUnet segmentation model for Covid-19 lung infection regions in CT images. Neural Comput & Applic 36, 7265–7278 (2024). https://doi.org/10.1007/s00521-024-09459-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-024-09459-7

Keywords

Search

Navigation