Skip to main content

Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images

  • Conference paper
  • First Online:
Head and Neck Tumor Segmentation (HECKTOR 2020)

Abstract

Development of robust and accurate fully automated methods for medical image segmentation is crucial in clinical practice and radiomics studies. In this work, we contributed an automated approach for Head and Neck (H&N) primary tumor segmentation in combined positron emission tomography/computed tomography (PET/CT) images in the context of the MICCAI 2020 Head and Neck Tumor segmentation challenge (HECKTOR). Our model was designed on the U-Net architecture with residual layers and supplemented with Squeeze-and-Excitation Normalization. The described method achieved competitive results in cross-validation (DSC 0.745, precision 0.760, recall 0.789) performed on different centers, as well as on the test set (DSC 0.759, precision 0.833, recall 0.740) that allowed us to win first prize in the HECKTOR challenge among 21 participating teams. The full implementation based on PyTorch and the trained models are available at https://github.com/iantsen/hecktor.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Similar content being viewed by others

References

  1. Andrearczyk, V., et al.: Overview of the HECKTOR challenge at MICCAI 2020: automatic Head and Neck Tumor Segmentation in PET/CT (2021)

    Google Scholar 

  2. Andrearczyk, V., et al.: Automatic segmentation of head and neck tumors and nodal metastases in PET-CT scans. In: Medical Imaging with Deep Learning (MIDL) (2020)

    Google Scholar 

  3. Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)

    Article  Google Scholar 

  4. Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: the missing ingredient for fast stylization, arXiv preprint arXiv:1607.08022 (2016)

  5. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks, CoRR, vol. abs/1709.01507 (2017). http://arxiv.org/abs/1709.01507

  6. Iantsen, A., Jaouen, V., Visvikis, D., Hatt, M.: Squeeze-and-excitation normalization for brain tumor segmentation. In: International MICCAI Brainlesion Workshop (2020)

    Google Scholar 

  7. 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 

  8. 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 

  9. Milletari, F., Navab, N., Ahmadi, S.-A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: International Conference on 3D Vision, pp. 565–571. IEEE (2016)

    Google Scholar 

  10. Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal Loss for Dense Object Detection, arXiv preprint arXiv:1708.02002 (2017)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andrei Iantsen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Iantsen, A., Visvikis, D., Hatt, M. (2021). Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images. In: Andrearczyk, V., Oreiller, V., Depeursinge, A. (eds) Head and Neck Tumor Segmentation. HECKTOR 2020. Lecture Notes in Computer Science(), vol 12603. Springer, Cham. https://doi.org/10.1007/978-3-030-67194-5_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-67194-5_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-67193-8

  • Online ISBN: 978-3-030-67194-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics