Skip to main content
SpringerLink
Log in

CANet: Channel Extending and Axial Attention Catching Network for Multi-structure Kidney Segmentation

  • Conference paper
  • First Online:
Lesion Segmentation in Surgical and Diagnostic Applications (CuRIOUS 2022, KiPA 2022, MELA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13648))

Abstract

Renal cancer is one of the most prevalent cancers worldwide. Clinical signs of kidney cancer include hematuria and low back discomfort, which are quite distressing to the patient. Some surgery-based renal cancer treatments like laparoscopic partial nephrectomy relys on the 3D kidney parsing on computed tomography angiography (CTA) images. Many automatic segmentation techniques have been put forward to make multi-structure segmentation of the kidneys more accurate. The 3D visual model of kidney anatomy will help clinicians plan operations accurately before surgery. However, due to the diversity of the internal structure of the kidney and the low grey level of the edge. It is still challenging to separate the different parts of the kidney in a clear and accurate way. In this paper, we propose a channel extending and axial attention catching Network(CANet) for multi-structure kidney segmentation. Our solution is founded based on the thriving nn-UNet architecture. Firstly, by extending the channel size, we propose a larger network, which can provide a broader perspective, facilitating the extraction of complex structural information. Secondly, we include an axial attention catching(AAC) module in the decoder, which can obtain detailed information for refining the edges. We evaluate our CANet on the KiPA2022 dataset, achieving the dice scores of 95.8%, 89.1%, 87.5% and 84.9% for kidney, tumor, artery and vein, respectively, which helps us get fourth place in the challenge.

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 44.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 59.99
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. Cheng, M.M.C., et al.: Nanotechnologies for biomolecular detection and medical diagnostics. Curr. Opin. Chem. Biol. 10(1), 11–19 (2006)

    Article  Google Scholar 

  2. Chow, W.H., Dong, L.M., Devesa, S.S.: Epidemiology and risk factors for kidney cancer. Nat. Rev. Urol. 7(5), 245–257 (2010)

    Article  Google Scholar 

  3. Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49

    Chapter  Google Scholar 

  4. Dong, Z., et al.: MNet: rethinking 2D/3D networks for anisotropic medical image segmentation. arXiv preprint arXiv:2205.04846 (2022)

  5. He, Y., et al.: DPA-DenseBiasNet: semi-supervised 3d fine renal artery segmentation with dense biased network and deep priori anatomy. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 139–147. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_16

    Chapter  Google Scholar 

  6. He, Y., et al.: Dense biased networks with deep priori anatomy and hard region adaptation: semi-supervised learning for fine renal artery segmentation. Med. Image Anal. 63, 101722 (2020)

    Article  Google Scholar 

  7. He, Y., et al.: Meta grayscale adaptive network for 3D integrated renal structures segmentation. Med. Image Anal. 71, 102055 (2021)

    Article  Google Scholar 

  8. Isensee, F., et al.: nnU-Net: self-adapting framework for U-Net-based medical image segmentation. arXiv preprint arXiv:1809.10486 (2018)

  9. Jin, C., et al.: 3D fast automatic segmentation of kidney based on modified AAM and random forest. IEEE Trans. Med. Imaging 35(6), 1395–1407 (2016)

    Article  Google Scholar 

  10. Li, J., Lo, P., Taha, A., Wu, H., Zhao, T.: Segmentation of renal structures for image-guided surgery. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 454–462. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_52

    Chapter  Google Scholar 

  11. Lin, D.T., Lei, C.C., Hung, S.W.: Computer-aided kidney segmentation on abdominal CT images. IEEE Trans. Inf Technol. Biomed. 10(1), 59–65 (2006)

    Article  Google Scholar 

  12. Ljungberg, B., et al.: European association of urology guidelines on renal cell carcinoma: the 2019 update. Eur. Urol. 75(5), 799–810 (2019)

    Article  Google Scholar 

  13. Luu, H.M., Park, S.H.: Extending nn-UNet for brain tumor segmentation. In: Crimi, A., Bakas, S. (eds.) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol. 12963, pp. pp. 173–186. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-09002-8_16

  14. 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 

  15. Shao, P., et al.: Laparoscopic partial nephrectomy with segmental renal artery clamping: technique and clinical outcomes. Eur. Urol. 59(5), 849–855 (2011)

    Article  Google Scholar 

  16. Shao, P., et al.: Precise segmental renal artery clamping under the guidance of dual-source computed tomography angiography during laparoscopic partial nephrectomy. Eur. Urol. 62(6), 1001–1008 (2012)

    Article  Google Scholar 

  17. Taha, A., Lo, P., Li, J., Zhao, T.: Kid-Net: convolution networks for kidney vessels segmentation from CT-volumes. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 463–471. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_53

    Chapter  Google Scholar 

  18. Wang, C., et al.: Tensor-cut: a tensor-based graph-cut blood vessel segmentation method and its application to renal artery segmentation. Med. Image Anal. 60, 101623 (2020)

    Article  Google Scholar 

  19. Wang, H., Zhu, Y., Green, B., Adam, H., Yuille, A., Chen, L.-C.: Axial-DeepLab: stand-alone axial-attention for panoptic segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12349, pp. 108–126. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58548-8_7

    Chapter  Google Scholar 

  20. Wang, K.N., et al.: AWSnet: an auto-weighted supervision attention network for myocardial scar and edema segmentation in multi-sequence cardiac magnetic resonance images. Med. Image Anal. 77, 102362 (2022)

    Article  Google Scholar 

  21. Zhou, G.Q., et al.: Learn fine-grained adaptive loss for multiple anatomical landmark detection in medical images. IEEE J. Biomed. Health Inform. 25(10), 3854–3864 (2021)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Monash University, Southeast University, School of Biological Science and Medical Engineering, Nanjing, China

    Zhenyu Bu, Kaini Wang & Guangquan Zhou

Authors
  1. Zhenyu Bu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kaini Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guangquan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guangquan Zhou .

Editor information

Editors and Affiliations

  1. Concordia University, Montréal, QC, Canada

    Yiming Xiao

  2. Southeast University, Nanjing, China

    Guanyu Yang

  3. Wuhan University, Wuhan, China

    Shuang Song

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bu, Z., Wang, K., Zhou, G. (2023). CANet: Channel Extending and Axial Attention Catching Network for Multi-structure Kidney Segmentation. In: Xiao, Y., Yang, G., Song, S. (eds) Lesion Segmentation in Surgical and Diagnostic Applications. CuRIOUS KiPA MELA 2022 2022 2022. Lecture Notes in Computer Science, vol 13648. Springer, Cham. https://doi.org/10.1007/978-3-031-27324-7_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-27324-7_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-27323-0

  • Online ISBN: 978-3-031-27324-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation