Skip to main content
SpringerLink
Log in

Joint Skeleton and Boundary Features Networks for Curvilinear Structure Segmentation

  • 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:

  • 912 Accesses

Abstract

Curvilinear structure segmentation has wide-ranging practical applications across many fields. However, existing methods have low topological accuracy when segmenting curved structures, and face difficulties in maintaining complete topological connectivity in their segmentation results. To address these problems, we propose a joint skeleton and boundary feature Encoder-Decoder segmentation network for curved structures. Our method incorporates three decoding branches that extract semantic, skeleton, and boundary features, respectively. Additionally, each decoder output undergoes feature fusion via a joint unit after every layer. Furthermore, adaptive connection units are added between the encoder and decoder to selectively capture information from the encoder. Finally, we perform evaluations on three public datasets for curvilinear structure segmentation tasks, including retinal images for clinical diagnosis, coronary angiography images, and road crack images. Experimental results show that the method outperforms other existing state-of-the-art methods in terms of pixel-level accuracy and topological connectivity.

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. Sau, P.C., Bansal, A.: A novel diabetic retinopathy grading using modified deep neu-ral network with segmentation of blood vessels and retinal abnormalities. Multimedia Tools and Applications 81(27), 39605–39633 (2022)

    Article  Google Scholar 

  2. Kar, M.K., Neog, D.R., Nath, M.K.: Retinal vessel segmentation using multi-scale residual convolutional neural network (msr-net) combined with generative adversarial networks. Circuits Systems Signal Process. 42(2), 1206–1235 (2023)

    Article  Google Scholar 

  3. Yang, F., Zhang, L., Yu, S., Prokhorov, D., Mei, X., Ling, H.: Feature pyramid and hierarchical boosting network for pavement crack-detection. IEEE Trans. Intell. Transp. Syst. 21(4), 1525–1535 (2019)

    Article  Google Scholar 

  4. Batra, A., Singh, S., Pang, G., Basu, S., Jawahar, C., Paluri, M.: Improved road connectivity by joint learning of orientation and segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10385–10393 (2019)

    Google Scholar 

  5. Ventura, C., Pont-Tuset, J., Caelles, S., Maninis, K.K., Van Gool, L.: Iterative deep learning for road topology extraction. arXiv pre-print arXiv:1808.09814 (2018)

  6. Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)

    Article  Google Scholar 

  7. Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833–851. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_49

    Chapter  Google Scholar 

  8. Li, K., Qi, X., Luo, Y., Yao, Z., Zhou, X., Sun, M.: Accurate retinal vessel segmentation in color fundus images via fully attention-based networks. IEEE J. Biomed. Health Inform. 25(6), 2071–2081 (2020)

    Article  Google Scholar 

  9. Zhu, Z., Xu, M., Bai, S., Huang, T., Bai, X.: Asymmetric non-local neural networks for semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 593–602 (2019)

    Google Scholar 

  10. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431–3440 (2015)

    Google Scholar 

  11. Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolu-tional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)

    Article  Google Scholar 

  12. Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional net-works for biomedical image segmentation. In: Medical Image Com-puting and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18, pp. 234–241. Springer (2015)

    Google Scholar 

  13. Oktay, O., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)

  14. Alom, M.Z., Yakopcic, C., Hasan, M., Taha, T.M., Asari, V.K.: Recurrent residual u-net for medical image segmentation. Journal of Medical Imaging 6(1), 014006 (2019)

    Article  Google Scholar 

  15. Wang, W., Yu, K., Hugonot, J., Fua, P., Salzmann, M.: Recurrent u-net for resource-constrained segmentation. In: Proceedings of the IEEE/CVF international conference on computer vision, pp. 2142–2151 (2019)

    Google Scholar 

  16. Li, H., et al.: Structure-consistent restoration network for cataract fundus image enhancement. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part II. pp, 487–496. Springer(2022)

    Google Scholar 

  17. Wang, C., Xu, R., Xu, S., Meng, W., Zhang, X.: Da-net: Dual branch transformer and adaptive strip upsampling for retinal vessels segmentation. In: Medical Image Computing and Computer Assist-ed Intervention–MICCAI 2022: 25th International Conference, Sin-gapore, September 18–22, 2022, Proceedings, Part II, pp. 528–538. Springer (2022)

    Google Scholar 

  18. Jerripothula, K.R., Cai, J., Lu, J., Yuan, J.: Object co-skeletonization with co-segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3881–3889. IEEE (2017)

    Google Scholar 

  19. Wshah, S., Shi, Z., Govindaraju, V.: Segmentation of arabic handwriting based on both contour and skeleton segmentation. In: 2009 10th International Conference on Document Analysis and Recognition, pp. 793–797. IEEE (2009)

    Google Scholar 

  20. Jain, V., et al.: Boundary learning by optimization with topological constraints. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2488–2495. IEEE (2010)

    Google Scholar 

  21. Xu, Z., Sun, Y., Liu, M.: Topo-boundary: A benchmark dataset on topological road-boundary detection using aerial images for autonomous driving. IEEE Robotics and Automation Letters 6(4), 7248–7255 (2021)

    Article  Google Scholar 

  22. Zhang, T.Y., Suen, C.Y.: A fast parallel algorithm for thinning digital patterns. Com-munications of the ACM 27(3), 236–239 (1984)

    Article  Google Scholar 

  23. Staal, J., Abr`amoff, M.D., Niemeijer, M., Viergever, M.A., Van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE transactions on medical imaging 23(4), 501–509 (2004)

    Google Scholar 

  24. Ma, Y., et al.: Self-supervised vessel segmentation via adversarial learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7536–7545 (2021)

    Google Scholar 

  25. Zou, Q., Cao, Y., Li, Q., Mao, Q., Wang, S.: Cracktree: Automatic crack detection from pavement images. Pattern Recogn. Lett. 33(3), 227–238 (2012)

    Article  Google Scholar 

  26. Ara´ujo, R.J., Cardoso, J.S., Oliveira, H.P.: A deep learning design for improving to-pology coherence in blood vessel segmentation. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part I 22, pp. 93–101. Springer (2019)

    Google Scholar 

  27. Wegner, J.D., Montoya-Zegarra, J.A., Schindler, K.: A higher-order crf model for road network extraction. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1698–1705 (2013)

    Google Scholar 

  28. Li, L., Verma, M., Nakashima, Y., Nagahara, H., Kawasaki, R.: Iternet: retinal image segmentation utilizing structural redundancy in vessel networks. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp. 3656–3665 (2020)

    Google Scholar 

  29. Cheng, M., Zhao, K., Guo, X., Xu, Y., Guo, J.: Joint topology-preserving and feature-refinement network for curvilinear structure segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7147–7156 (2021)

    Google Scholar 

  30. Liu, W., et al.: Full-resolution network and dual-threshold iteration for retinal vessel and coronary angiograph seg-mentation. IEEE J. Biomed. Health Inform. 26(9), 4623–4634 (2022)

    Article  Google Scholar 

Download references

Acknowledgment

This work was supported by National Natural Science Foundation of China (62271359).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China

    Yubo Wang, Li Chen, Zhida Feng & Yunxiang Cao

  2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan University of Science and Technology, Wuhan, China

    Yubo Wang, Li Chen, Zhida Feng & Yunxiang Cao

Authors
  1. Yubo Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhida Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yunxiang Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Li Chen .

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

Wang, Y., Chen, L., Feng, Z., Cao, Y. (2023). Joint Skeleton and Boundary Features Networks for Curvilinear Structure Segmentation. 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_20

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-4761-4_20

  • 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