Skip to main content
SpringerLink
Log in

Multi-scale organs image segmentation method improved by squeeze-and-attention based on partially supervised learning

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Radiotherapy is one of most treatments for tumors. To accurately control the radiation dose distribution and lessen the radiation damage to normal tissues and organs in radiotherapy, it is essential to delineate organs at risk (OARs) precisely. However, manual delineating and some traditional methods are labor-intensive and time-consuming. There is an urgent need for fast and precise segmentation methods in radiotherapy.

Methods

This paper proposes a fully automatic segmentation method based on the 3D U-Net for multi-organ in head and neck. It introduces squeeze-and-attention blocks to gather multi-scale context information and the receptive field block to balance the performance between large-sized and small-sized organs. Furthermore, it is trained by the marginal and exclusion loss function in a partially supervised learning mode.

Results

We evaluated the model with dice similarity coefficient (DSC), 95% Hausdorff distance (95HD) and inference time. Its average DSC is 0.829, which is 4.5%, 3.2%, and 2.4% higher than AnatomyNet’s, nnU-net’s, and FocusNet’s, respectively, and its average 95HD is 2.19. Moreover, its inference time and parameters are 63% and 60% less than FocusNetv2’s.

Conclusion

For the segmentation of OARs in head and neck, our model is more accurate than AnatomyNet, faster than FocusNetv2, and better balances between segmentation accuracy and inference time. It demonstrates that our method is more applicable for clinical treatment.

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
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Jemal A, Bray F, Center MM (2011) Global cancer statistics. CA Cancer J Clin 61(2):69–90. https://doi.org/10.3322/caac.20107

    Article  PubMed  Google Scholar 

  2. Çiçek Ö, Abdulkadir A, Lienkamp S.S., Brox T, Ronneberger O (2016) 3d u-net: Learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) Int. Conf. on Medical Image Computing and Computer-assisted Intervention, pp. 424–432. Springer, Cham . https://doi.org/10.1007/978-3-319-46723-8_49. https://link.springer.com/chapter/10.1007%2F978-3-319-46723-8_49

  3. Zhong Z, Lin Z.Q., Bidart R, Hu X, Daya I.B., Li Z, Zheng W.S., Li J, Wong A (2020) Squeeze-and-attention networks for semantic segmentation. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13062–13071 . https://doi.org/10.1109/CVPR42600.2020.01308

  4. Liu S, Huang D, Wang Y (2018) Receptive field block net for accurate and fast object detection. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision –ECCV 2018, pp. 404–419. Springer, Cham . https://doi.org/10.1007/978-3-030-01252-6_24

  5. Shi G, Xiao L, Chen Y, Zhou S.K. (2021) Marginal loss and exclusion loss for partially supervised multi-organ segmentation. Medical Image Analysis 70, 101979 . https://doi.org/10.1016/j.media.2021.101979

  6. Ren X, Lei X, Dong N, Shao Y, Zhang H, Shen D, Qian W (2018) Interleaved 3d-cnns for joint segmentation of small-volume structures in head and neck ct images. Medical Physics 45(5), 2063–2075 . https://doi.org/10.1002/mp.12837

  7. Vrtovec T, Močnik D, Strojan P, Pernuš F, Ibragimov B (2020) Auto-segmentation of organs at risk for head and neck radiotherapy planning: From atlas-based to deep learning methods. Medical Physics 47(9), 929–950 . https://doi.org/10.1002/mp.14320

  8. Gou S, Tong N, Qi S, Yang S, Chin R, Sheng K (2020) Self-channel-and-spatial-attention neural network for automated multi-organ segmentation on head and neck CT images. Physics in Medicine & Biology 65(24), 245034 . https://doi.org/10.1088/1361-6560/ab79c3

  9. Zhu W, Huang Y, Liang Z, Chen X, Yong L, Zhen Q, Nan D, Wei F, Xie X (2018) Anatomynet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Medical Physics 46(2), 576–589 . https://doi.org/10.1088/1361-6560/abd953

  10. Hu J, Shen L, Albanie S, Sun G, Wu E (2020) Squeeze-and-excitation networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 42(8), 2011–2023 . https://doi.org/10.1109/TPAMI.2019.2913372

  11. Shelhamer E, Long J, Darrell T (2017) Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 39(4), 640–651 . https://doi.org/10.1109/TPAMI.2016.2572683

  12. Lin T-Y, Goyal P, Girshick R, He K, Dollár P (2020) Focal loss for dense object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 42(2):318–327. https://doi.org/10.1109/TPAMI.2018.2858826

    Article  PubMed  Google Scholar 

  13. Tappeiner E, Pröll S, Hönig M, Raudaschl P.F., Zaffino P, Spadea M.F., Sharp G.C., Schubert R, Fritscher K (2019) Multi-organ segmentation of the head and neck area: an efficient hierarchical neural networks approach. International Journal of Computer Assisted Radiology and Surgery 14, 745–759 . https://doi.org/10.1007/s11548-019-01922-4

  14. Gao Y, Huang R, Chen M, Wang Z, Deng J, Chen Y, Yang Y, Zhang J, Tao C, Li H (2019) FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images. https://arxiv.org/abs/1907.12056v1

  15. Gao Y, Huang R, Yang Y, Zhang J, Shao K, Tao C, Chen Y, Metaxas D.N., Li H, Chen M (2021) Focusnetv2: Imbalanced large and small organ segmentation with adversarial shape constraint for head and neck ct images. Medical Image Analysis 67, 101831. https://doi.org/10.1016/j.media.2020.101831

  16. Liu, Y., Lei, Y., Fu, Y., Wang, T., Zhou, J., Jiang, X., McDonald, M., Beitler, J.J., Curran, W.J., Liu, T., Yang, X.: Head and neck multi–organ auto-segmentation on ct images aided by synthetic mri. Medical Physics 47(9), 4294–4302 (2020). https://doi.org/10.1002/mp.14378

  17. Xu, X., Chen, J., Zhang, H., Han, G.: Dual pyramid network for salient object detection. Neurocomputing 375, 113–123 (2020). https://doi.org/10.1016/j.neucom.2019.09.077

  18. Dai, X., Lei, Y., Wang, T., Dhabaan, A.H., McDonald, M., Beitler, J.J., Curran, W.J., Zhou, J., Liu, T., Yang, X.: Head-and-neck organs-at-risk auto-delineation using dual pyramid networks for CBCT-guided adaptive radiotherapy. Physics in Medicine & Biology 66(4), 045021 (2021). https://doi.org/10.1088/1361-6560/abd953

  19. Raudaschl, P., Zaffino, P., Sharp, G., Spadea, M., Chen, A., Dawant, B.M., Albrecht, T., Gass, T., Langguth, C., Lüthi, M., Jung, F., Knapp, O., Wesarg, S., Mannion-Haworth, R., Bowes, M., Ashman, A., Guillard, G., Brett, A., Vincent, G., Orbes-Arteaga, M., Cárdenas-Pen̄a, D., Castellanos-Dominguez, G., Aghdasi, N., Li, Y., Berens, A., Hannaford, B., Schubert, R., Fritscher, K.D.: Evaluation of segmentation methods on head and neck ct: Auto-segmentation challenge 2015. Medical Physics 44(5), 2020–2036 (2017). https://doi.org/10.1002/mp.12197

  20. Clark, Vendt, Smith, Freymann, Kirby, Koppel, Moore, Phillips, Maffitt, and, P. (2013) The cancer imaging archive (tcia): Maintaining and operating a public information repository. Digit Imaging 26, 1045–1057 . https://doi.org/10.1007/s10278-013-9622-7

  21. Vallières M, Kay-Rivest E, Perrin LJ, Liem X, Furstoss C, Aerts H, Khaouam N, Nguyen-Tan PF, Wang CS, Sultanem K (2017) Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. Scientific Reports 7(1):10117. https://doi.org/10.1038/s41598-017-10371-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2818–2826 . https://doi.org/10.1109/CVPR.2016.308

  23. Chen L.-C., Papandreou G, Kokkinos I, Murphy K, Yuille A.L. (2018) Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Transactions on Pattern Analysis and Machine Intelligence 40(4), 834–848 . https://doi.org/10.1109/TPAMI.2017.2699184

  24. Isensee F, Jaeger P.F., Kohl S.A.A., Petersen J, Maier-Hein K.H. (2021) nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods 18, 203–211 . https://doi.org/10.1038/s41592-020-01008-z

  25. Chen A, Dawant B (2016) A multi-atlas approach for the automatic segmentation of multiple structures in head and neck ct images . https://doi.org/10.54294/hk5bjs

  26. Albrecht T, Gass T, Langguth C, Lüthi M (2015) Multi atlas segmentation with active shape model refinement for multi-organ segmentation in head and neck cancer radiotherapy planning . https://doi.org/10.54294/kmcunc

Download references

Funding

We would like to thank the financial supports from National Natural Science Foundation of China (62175156, 61976140, 61675134, 81827807), Shanghai Committee of Science and Technology (1944190 5800), and Wenzhou Medical University Key Laboratory Open Project (K181002).

Author information

Authors and Affiliations

  1. School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, China

    Mao Hongdong, Cao Guogang, Zhang Shu, Liu Shunkun, Kong Deqing, Li Sicheng, Peng Zeyu, Wu Yan & Chen Ying

  2. School of Science, Shanghai Institute of Technology, Shanghai, China

    Dai Cuixia

Authors
  1. Mao Hongdong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cao Guogang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhang Shu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liu Shunkun
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kong Deqing
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Li Sicheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Peng Zeyu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Wu Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Chen Ying
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Dai Cuixia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cao Guogang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval

For the retrospective studies formal consent is not required.

Informed consent

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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hongdong, M., Guogang, C., Shu, Z. et al. Multi-scale organs image segmentation method improved by squeeze-and-attention based on partially supervised learning. Int J CARS 17, 1135–1142 (2022). https://doi.org/10.1007/s11548-022-02632-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-022-02632-0

Keywords

Search

Navigation