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CBAR-UNet: A novel methodology for segmentation of cardiac magnetic resonance images using block attention-based deep residual neural network

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Abstract

Computer-aided Medical Image Segmentation (MIS) plays a leading role in diagnosing diseases automatically. MIS is used extensively in diagnosing medical ailments to obtain clinically relevant information of the shapes and volumes of the target organs and tissues. In order to facilitate accurate segmentation, a Block Attention Based Deep Residual Neural Network, i.e., CBAR-UNet model, is proposed to perform cardiac image segmentation on short axis Magnetic Resonance Images (MRI) stacks from top to bottom slice. The Automated Cardiac Diagnosis Challenge (ACDC) dataset is used in the proposed work, which comprise of a 3D MRI of 100 patients (i.e., 200 MRI stacks). Further, 3D MRIs are converted into 2D by slicing each image to train the model, and Contrast Limited Adaptive Histogram Equalization (CLAHE) is performed on the 2D dataset. Residual connections are introduced that aid in training deeper models. Convolutional Block Attention Module (CBAM) is also added in the proposed network, which consists of Spatial and Channel attention that enhances the spatial and channel-wise features in the image by providing information like ‘what’ and ‘where’ to pay attention respectively. To test and validate the proposed methodology, extensive experimentations were conducted and it was observed that proposed image segmentation model delivers better results by 2.47% when compared to both 2D and 3D State-Of-The-Art (SOTA) methods with a Dice Score of 0.9428.

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Authors declare that all the data being used in the design and production cum layout of the manuscript is declared in the manuscript.

References

  1. Liu D, Jia Z, Jin M, Liu Q, Liao Z, Zhong J, Chen G (2020) Cardiac magnetic resonance image segmentation based on convolutional neural network. Comput Methods Programs Biomed 197:105755

    Article  Google Scholar 

  2. Liu J, Li M, Gong S, Mohammadzadeh A, Yang G (2023) Toward right ventricle segmentation in cardiac MRIs via feature multiplexing and multiscale weighted convolution. IEEE Journal of Biomedical and Health Informatics

  3. Faragallah OS, Abdel-Aziz G, El-Shafai W, El-Sayed HS, El-Zoghdy SF, Geweid GG (2021) Performance evaluation of medical segmentation techniques for Cardiac MRI. Intell Autom Soft Comput, 29(1)

  4. Shirly S, Ramesh K (2019) Review on 2D and 3D MRI image segmentation techniques. Curr Med Imaging 15(2):150–160

    Article  Google Scholar 

  5. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5–9, 2015, proceedings, part III 18 (pp. 234–241). Springer International Publishing

  6. Elyan E, Vuttipittayamongkol P, Johnston P, Martin K, McPherson K, Jayne C, Sarker MK (2022) Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward. Artificial Intelligence Surgery, 2

  7. Conze PH, Andrade-Miranda G, Singh VK, Jaouen V, Visvikis D (2023) Current and emerging trends in medical image segmentation with deep learning. IEEE Trans Radiation Plasma Med Sci

  8. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30

  9. Cardiovascular Diseases (CVDs). Word Health Organization (2021) [Online] https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)

  10. Winther HB, Hundt C, Schmidt B, Czerner C, Bauersachs J, Wacker F, Vogel-Claussen J (2017) $u $-net: Deep Learning for Generalized Biventricular Cardiac Mass and Function Parameters. arXiv preprint arXiv:1706.04397

  11. Baumgartner CF, Koch LM, Pollefeys M, Konukoglu E (2018) An exploration of 2D and 3D deep learning techniques for cardiac MR image segmentation. In Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges: 8th International Workshop, STACOM 2017, Held in Conjunction with MICCAI 2017, Quebec City, Canada, September 10–14, 2017, Revised Selected Papers 8 (pp. 111–119). Springer International Publishing

  12. Tran PV (2016) A fully convolutional neural network for cardiac segmentation in short-axis MRI. arXiv preprint arXiv:1604.00494

  13. Bai W, Sinclair M, Tarroni G, Oktay O, Rajchl M, Vaillant G, Rueckert D (2018) Automated cardiovascular magnetic resonance image analysis with fully convolutional networks. Journal of cardiovascular magnetic resonance 20(1):65

    Article  Google Scholar 

  14. Isensee F, Jaeger PF, Full PM, Wolf I, Engelhardt S, Maier-Hein KH (2018) Automatic cardiac disease assessment on cine-MRI via time-series segmentation and domain specific features. In Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges: 8th International Workshop, STACOM 2017, Held in Conjunction with MICCAI 2017, Quebec City, Canada, September 10–14, 2017, Revised Selected Papers 8 (pp. 120–129). Springer International Publishing

  15. Lan Y, Jin R (2019) Automatic segmentation of the left ventricle from cardiac MRI using deep learning and double snake model. IEEE Access 7:128641–128650

  16. Du X, Tang R, Yin S, Zhang Y, Li S (2018) Direct segmentation-based full quantification for left ventricle via deep multi-task regression learning network. IEEE J Biomedical Health Inf 23(3):942–948

    Article  Google Scholar 

  17. Zheng Q, Delingette H, Duchateau N, Ayache N (2018) 3-D consistent and robust segmentation of cardiac images by deep learning with spatial propagation. IEEE Trans Med Imaging 37(9):2137–2148

    Article  Google Scholar 

  18. Zhou HY, Guo J, Zhang Y, Yu L, Wang L, Yu Y (2021) nnformer: Interleaved transformer for volumetric segmentation. arXiv preprint arXiv:2109.03201

  19. Fan C, Su Q, Xiao Z, Su H, Hou A, Luan B (2023) ViT-FRD: a vision transformer model for cardiac MRI image segmentation based on feature recombination distillation. IEEE Access

  20. Yang R, Liu K, Liang Y (2024) A fusion-attention swin transformer for cardiac MRI image segmentation. IET Image Proc 18(1):105–115

    Article  Google Scholar 

  21. Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7132–7141)

  22. Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q (2020) ECA-Net: Efficient channel attention for deep convolutional neural networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 11534–11542)

  23. 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)

  24. Zhang Z, Wang M (2022) Convolutional neural network with convolutional block attention module for finger vein recognition. arXiv preprint arXiv:2202.06673

  25. Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges:8th International Workshop, STACOM 2017, Held in Conjunction with MICCAI 2017, Quebec City, Canada, September 10–14, 2017, Revised Selected Papers (Vol. 10663). Springer

  26. Bernard O, Lalande A, Zotti C, Cervenansky F, Yang X, Heng PA, Jodoin PM (2018) Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE transactions on medical imaging 37(11):2514–2525

    Article  Google Scholar 

  27. Xu G, Zhang X, He X, Wu X (2023), October Levit-unet: Make faster encoders with transformer for medical image segmentation. In Chinese Conference on Pattern Recognition and Computer Vision (PRCV) (pp. 42–53). Singapore: Springer Nature Singapore

  28. Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., ... & Zhou, Y. (2021). Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306

  29. Jang Y, Hong Y, Ha S, Kim S, Chang HJ (2018) Automatic segmentation of LV and RV in cardiac MRI. In Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges: 8th International Workshop, STACOM 2017, Held in Conjunction with MICCAI 2017, Quebec City, Canada, September 10–14, 2017, Revised Selected Papers 8 (pp. 161–169). Springer International Publishing

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Chandigarh University, Punjab, 140413, India

    Rakesh Kumar, Meenu Gupta & Aman Agarwal

  2. Graduate School, Faculty of Information Technology, Duy Tan University, Da Nang, 550000, Vietnam

    Anand Nayyar

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  1. Rakesh Kumar
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Correspondence to Anand Nayyar.

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Kumar, R., Gupta, M., Agarwal, A. et al. CBAR-UNet: A novel methodology for segmentation of cardiac magnetic resonance images using block attention-based deep residual neural network. Multimed Tools Appl 83, 85047–85063 (2024). https://doi.org/10.1007/s11042-024-19432-0

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  • DOI: https://doi.org/10.1007/s11042-024-19432-0

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