Skip to main content
SpringerLink
Log in

A ConvMixEst and Multi-attention UNet for Intervertebral Disc Segmentation in Multi-modal MRI

  • Conference paper
  • First Online:
Advanced Intelligent Computing Technology and Applications (ICIC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14090))

Included in the following conference series:

Abstract

Accurate segmentation of spinal magnetic resonance imaging (MRI) plays a critical role in the diagnosis and evaluation of intervertebral discs. However, accurate disc segmentation is not easy due to the overly trivial and high similarity of disc tissues, as well as the huge variability between slices, and multi-modal MRI disc segmentation is even more challenging. For this reason, we propose a model called ConvMixEst and Muti-Attention Unet (CAM-Unet), which combines MLP with the attentional mechanisms of Inverted Variational Attention (IVA) and Dilated Gated Attention (DGA). Specifically, in this work, we propose the IVA module for detailing the overall feature information of the dataset and capturing feature information at different scales, and also design a ConvMixEst for enhancing the global context information. After doing trials on the MICCAI-2018 IVD challenge dataset, we obtain Dice similarity coefficient equal to 92.53(%) and Jaccard coefficient equal to 86.10(%) and Precision equal to 94.09(%).

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Dutande, P., Baid, U., Talbar, S.: Deep residual separable convolutional neural network for lung tumor segmentation. Comput. Biol. Med. 141, 105161 (2022). https://doi.org/10.1016/j.compbiomed.2021.105161

    Article  Google Scholar 

  2. Cheng, J., Tian, S., Yu, L., et al.: ResGANet: residual group attention network for medical image classification and segmentation. Med. Image Anal. 76, 102313 (2022). https://doi.org/10.1016/j.media.2021.102313

    Article  Google Scholar 

  3. Punn, N.S., Agarwal, S.: RCA-IUnet: a residual cross-spatial attention-guided inception U-Net model for tumor segmentation in breast ultrasound imaging. Mach. Vis. Appl. 33(2), 37 (2022). https://doi.org/10.1007/s00138-022-01280-3

    Article  Google Scholar 

  4. Dolz, J., Desrosiers, C., Ben Ayed, I.: IVD-Net: intervertebral disc localization and segmentation in MRI with a multi-modal UNet. In: Zheng, G., Belavy, D., Cai, Y., Li, S. (eds.) CSI 2018. LNCS, vol. 11397, pp. 130–143. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-13736-6_11

    Chapter  Google Scholar 

  5. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  6. Zhao, X., Jia, H., Pang, Y., et al.: M2SNet: multi-scale in multi-scale subtraction network for medical image segmentation. arXiv preprint arXiv:2303.10894 (2023). https://doi.org/10.48550/arXiv.2303.10894

  7. Tang, F., Wang, L., Ning, C., Xian, M., Ding, J.: CMU-Net: a strong ConvMixer-based medical ultrasound image segmentation network. arXiv preprint arXiv:2210.13012 (2022). https://doi.org/10.48550/arXiv.2210.13012

  8. Zhang, Y., Yuan, L., Wang, Y., Zhang, J.: SAU-Net: efficient 3D spine MRI segmentation using inter-slice attention. In: Medical Imaging with Deep Learning, pp. 903–913 (2020)

    Google Scholar 

  9. Li, C., Liu, T., Chen, Z., et al.: SPA-RESUNET: strip pooling attention resunet for multi-class segmentation of vertebrae and intervertebral discs. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp. 1–5 (2022). https://doi.org/10.1109/ISBI52829.2022.9761577

  10. Valanarasu, J.M.J., Patel, V.M.: UNeXt: MLP-based rapid medical image segmentation network. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. LNCS, vol. 13435, pp. 23–33. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16443-9_3

  11. Ruan, J., Xiang, S., Xie, M., Liu, T., Fu, Y.: MALUNet: a multi-attention and light-weight unet for skin lesion segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1150–1156 (2022). https://doi.org/10.1109/BIBM55620.2022.9995040

  12. Kim, S., Bae, W.C., Masuda, K., Chung, C.B., Hwang, D.: Fine-grain segmentation of the intervertebral discs from MR spine images using deep convolutional neural networks: BSU-Net. Appl. Sci. 8(9), 1656 (2018). https://doi.org/10.3390/app8091656

    Article  Google Scholar 

  13. Das, P., Pal, C., Acharyya, A., et al.: Deep neural network for automated simultaneous intervertebral disc (IVDs) identification and segmentation of multi-modal MR images. Comput. Methods Programs Biomed. 205, 106074 (2021). https://doi.org/10.1016/j.cmpb.2021.106074

    Article  Google Scholar 

  14. Mader, A.O., Lorenz, C., Meyer, C.: A general framework for localizing and locally segmenting correlated objects: a case study on intervertebral discs in multi-modality MR images. In: Zheng, Y., Williams, B.M., Chen, K. (eds.) MIUA 2019. CCIS, vol. 1065, pp. 364–376. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39343-4_31

    Chapter  Google Scholar 

  15. Šušteršič, T., Ranković, V., Milovanović, V., Kovačević, V., Rasulić, L., Filipović, N.: A deep learning model for automatic detection and classification of disc herniation in magnetic resonance images. IEEE J. Biomed. Health Inform. 26(12), 6036–6046 (2022). https://doi.org/10.1109/JBHI.2022.3209585

  16. Liaskos, M., Savelonas, M.A., Asvestas, P.A., Lykissas, M.G., Matsopoulos, G.K.: Bimodal CT/MRI-based segmentation method for intervertebral disc boundary extraction. Information 11(9), 448 (2020). https://doi.org/10.3390/info11090448

    Article  Google Scholar 

  17. Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856–1867 (2019). https://doi.org/10.1109/TMI.2019.2959609

    Article  Google Scholar 

  18. Huang, H., Lin, L., Tong, R., et al.: Unet 3+: a full-scale connected Unet for medical image segmentation. In: ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1055–1059 (2020). https://doi.org/10.1109/ICASSP40776.2020.9053405

  19. Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, Shaanxi Normal University, Xi’an, China

    Sipei Lu, Hanqiang Liu & Xiangkai Guo

Authors
  1. Sipei Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hanqiang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiangkai Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hanqiang Liu .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Eastern Institute of Technology, Zhejiang, China

    De-Shuang Huang

  2. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  3. Zhengzhou University of Light Industry, Zhengzhou, China

    Baohua Jin

  4. Zhong Yuan University of Technology, Zhengzhou, China

    Boyang Qu

  5. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  6. Department of Computer Science, Liverpool John Moores University, Liverpool, UK

    Abir Hussain

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lu, S., Liu, H., Guo, X. (2023). A ConvMixEst and Multi-attention UNet for Intervertebral Disc Segmentation in Multi-modal MRI. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science(), vol 14090. Springer, Singapore. https://doi.org/10.1007/978-981-99-4761-4_12

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-4761-4_12

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-4760-7

  • Online ISBN: 978-981-99-4761-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation