Skip to main content
SpringerLink
Log in

HRNet:A hierarchical recurrent convolution neural network for retinal vessel segmentation

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The extraction of retinal vessel is of great importance in the diagnosis of fundus disease. Many approaches have been proposed for vessel segmentation. However, these models have some drawbacks. First, the encoder-decoder structures, U-Net i.e., will generate redundant information during successive convolution and sampling operations. Second, most methods only have feedforward process, and the feedback is also crucial for contextual feature representations from high to low layers. In this article, we overcome these limitations by proposing a hierarchical recurrent convolution neural network (HRNet). The proposed HRNet first integrates the advantage of the ResNet and Squeeze and Excitation (SE) to build SE-residual block in multi-scale layers, which capture the important channel-wise information and remove the redundant feature in deep network. Further, we design a hierarchical recurrent(feedback) mechanism to explore features from different upper to the lower layer by adding the output of each layer to its corresponding encoding layer iteratively. The feedback path encourages the feature reuse to improve the power of weak retinal vessel detection. Comprehensive experiments on three public retinal datasets (DRIVE, STARE and CHASE) demonstrate that the proposed HRNet is superior or equivalent to the state-of-art methods in terms of most of the indicators, including accuracy, F1-Score, sensitivity.

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Akram M, Khan SA (2013) Multilayered thresholding-based blood vessel seg-mentation for screening of diabetic retinopathy. Eng Comput 29 (2):165–173

    Article  Google Scholar 

  2. Al-Rawi M, Karajeh H (2007) Genetic algorithm matched filter optimization for automated detection of blood vessels from digital retinal images. Comput Methods Prog Biomed 87(3):248–253

    Article  Google Scholar 

  3. Al-Rawi M, Qutaishat M, Arrar M (2007) An improved matched filter for blood vessel detection of digital retinal images. Comput Biol Med 37(2):262–267

    Article  Google Scholar 

  4. AlDiri B, Hunter A, Steel D (2009) An active contour model for segmenting and measuring retinal vessels. IEEE Trans Med Imaging 28(9):1488–1497

    Article  Google Scholar 

  5. Alom MZ, Hasan M, Yakopcic C et al (2018) Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. In: IEEE conference on computer vision and pattern recognition (CVPR). arXiv:1802.06955

  6. Asadi M, Reza A, Fathy M et al (2020) Multi-level Context Gating of Embedded Collective Knowledge for Medical Image Segmentation. arXiv:2003.05056

  7. Azzopardi G, Strisciuglio N, Vento M (2015) Trainable COSFIRE filters for vessel delineation with application to retinal images. Med Image Anal 19 (1):46–57

    Article  Google Scholar 

  8. Can A, Shen H, Turmner J (1999) Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms. IEEE Trans Inform Technol Biomed 3(2):125–138

    Article  Google Scholar 

  9. Chang B, Meng L, Haber E et al (2018) Reversible architectures for arbitrarily deep residual neural networks. Thirty-Second National AAAI Conference on Artificial Intelligence. arXiv:1709.03698

  10. Chen Y (2017) A Labeling-Free approach to supervising deep neural networks for retinal blood vessel segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. arXiv:1704.07502

  11. Franklin SW, Rajan SE (2014) Retinal vessel segmentation employing ANN technique by Gabor and moment invariants-based features. Appl Soft Comput 22:94–100

    Article  Google Scholar 

  12. Fraz M, Remagnino P, Hoppe A (2012) Blood vessel segmentation methodologies in retinal images - a survey. IEEE Trans Med Imaging 108(1):407–433

    Google Scholar 

  13. Fu H, Xu Y, Lin S, Liu J (2016) Deepvessel: Retinal vessel segmentation via deep learning and conditional random field. In: International conference on medical image computing and computer-assisted intervention. pp 132–139

  14. Fu H, Xu Y, Wong K (2016) Retinal vessel segmentation via deep learning network and fully-connected conditional random fields. In: IEEE 13th international symposium on biomedical imaging (ISBI). pp 698–701

  15. Gu Z, Cheng J, Fu H et al (2019) CE-Net: Context Encoder Network for 2D Medical Image Segmentation. IEEE Trans Med Imaging 38(10):2281–2292. arXiv:1903.02740

    Article  Google Scholar 

  16. Han D, Kim J, Kim J (2017) Deep pyramidal residual networks

  17. He K, Zhang X et al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 770–778

  18. Hoover A, Kouznetsova V, Goldbaum M (2000) Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imaging 19(3):203–210

    Article  Google Scholar 

  19. Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132–7141

  20. Jin Q, Meng Z, Pham TD (2019) DUNEt: A deformable network for retinal vessel segmentation. Knowledge-based Systems 178(15):149–162. arXiv:1811.01206

    Article  Google Scholar 

  21. Joanna O (2018) Designing transparent and autonomous intelligent vision systems. Proceedings of the 11th International Conference on Agents and Artificial Intelligence 2:850–856. https://doi.org/10.5220/0007585208500856

    Article  Google Scholar 

  22. Li Q, Feng B, Xie LP et al (2016) A Cross-Modality learning approach for vessel segmentation in retinal images. IEEE Trans Med Imaging 35 (1):109–118

    Article  Google Scholar 

  23. Liskowski P, Krawiec K (2016) Segmenting retinal blood vessels with deep neural networks. IEEE Trans Med Imaging 35(11):2369–2380

    Article  Google Scholar 

  24. Liu I, Sun Y (1993) Recursive tracking of vascular networks in angiograms based on the detection-deletion scheme. IEEE Trans Med Imaging 12(2):334–341

    Article  Google Scholar 

  25. Long J, Shelhamer E, Darrell T (2015) Recurrent convolutional neural network for object recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440

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

  27. Lu X, Wang W, Ma C et al (2019) See more, Know More: Unsupervised Video Object Segmentation With Co-Attention Siamese Networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, DOI https://doi.org/10.1109/CVPR.2019.00374, (to appear in print)

  28. Lu X, Wang W, Shen J et al (2020) Zero-shot video object segmentation with co-attention siamese networks. In: IEEE Transactions on pattern analysis and machine intelligence, DOI https://doi.org/10.1109/TPAMI.2020.3040258, (to appear in print)

  29. Mendonca AM, Member S et al (2006) Segmentation of retinal blood vessels by combining the detection of centerlines and morpho-logical reconstruction. IEEE Trans Med Imaging 25(9):1200–1213

    Article  Google Scholar 

  30. Miri M, Mahloojifar A (2011) Retinal image analysis using Curvelet transform and multistructure elements morphology by reconstruction. IEEE Trans Biomed Eng 58(5):1183–1192

    Article  Google Scholar 

  31. Oliveira WS, Teixeira JV, Ren TI et al (2016) Unsupervised Retinal Vessel Segmentation Using Combined Filters. PLOS ONE 11(2):e0149943. https://doi.org/10.1371/journal.pone.0149943

    Article  Google Scholar 

  32. Orlando JI, Prokofyeva E, Blaschko MB (2017) A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images. IEEE Trans Biomed. Eng 64(1):16–27

    Article  Google Scholar 

  33. Pinheiro P, Collobert R (2014) Recurrent convolutional neural networks for scene labeling. Proc Int Conf Mach Learn 32(1):82–90

    Google Scholar 

  34. Pizer S, Amburn E, Austin D (1987) Adaptive histogram equalization and its variations. Comput Vision Graph Image Process 39(3):355–368

    Article  Google Scholar 

  35. Poudel RP, Lamata P, Montana G (2016) Recurrent fully convolutional neural networks for multi-slice mri cardiac segmentation. In: Reconstruction segmentation, and analysis of medical images, pp 83–94

  36. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. pp 234–241

  37. Roychowdhury S, Koozekanani D, Parhi K (2017) Blood vessel segmentation of fundus images by major vessel extraction and subimage classification. IEEE J Biomed Health Inform 19(3):1118–1128

    Google Scholar 

  38. Sasaki Y (2007) The truth of the F-measure. Teach Tutor Mater

  39. Soares J, Leandro J, Cesar R et al (2006) Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans Med Imaging 25(9):1214–1222

    Article  Google Scholar 

  40. Staal J, Abramoff M, Niemeijer M et al (2004) Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 23(4):501–509

    Article  Google Scholar 

  41. Valipour S, Siam M, Jagers M et al (2017) Recurrent fully convolutional networks for video segmentation. In: IEEE Winter conference on applications of computer vision (WACV)

  42. Visin F et al (2016) Reseg: a recurrent neural network-based model for semantic segmentation. In: Proceedings of the IEEE CVPRW, pp 41–48

  43. Vlachos M, Dermatas E (2010) Multi-scale retinal vessel segmentation using line tracking. Comput Med Imaging Graph 34(3):213–227

    Article  Google Scholar 

  44. Wang W, Lu X, Shen J et al (2019) Zero-Shot Video object segmentation via attentive graph neural networks. IEEE International Conference on Computer Vision(ICCV). arXiv:2001.06807

  45. Wang B, Qiu S, He H (2019) Dual encoding U-Net for retinal vessel segmentation. In: International conference on medical image computing and computer-assisted intervention, pp 84–92

  46. Wu Y, Xia Y, Song Y et al (2018) Multiscale network followed network model for retinal vessel segmentation. In: International conference on medical image computing and computer-assisted intervention (MICCAI), pp 119–126, DOI https://doi.org/10.1007/978-3-030-00934-214, (to appear in print)

  47. Wu Y, Xia Y, Song Y et al (2019) Vessel-net: retinal vessel segmentation under multi-path supervision. In: International conference on medical image computing and computer-assisted intervention, pp 264–272

  48. You X, Peng Q, Yuan Y et al (2011) Segmentation of retinal blood vessels using the radial projection and semi-supervised approach. Pattern Recognit 44 (10):2314–2324

    Article  Google Scholar 

  49. Zhang Y, Chung ACS (2018) Deep supervision with additional labels for retinal vessel segmentation task. Med Image Comput Comput Assist Interv 11071:83–91. https://doi.org/10.1007/978-3-030-00934-210

    Article  Google Scholar 

  50. Zhang B, Zhang L et al (2010) Retinal vessel extraction by matched filter with first- order derivative of Gaussian. Comput Biol Med 40(4):438–445

    Article  Google Scholar 

  51. Zhao Y, Rada L, Chen K et al (2015) Automated vessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images, vol 34, pp 1797–1807

  52. Zhu CZ, Xiang Y, Zou BJ et al (2014) Retinal vessel segmentation in fundus images using CART and AdaBoost. J Comput Aided Des Comput Graph 26(3):445–451

    Google Scholar 

  53. Zhuang J (2018) Laddernet: Multi-path networks based on u-net for medical image segmentation. arXiv:1810.07810

Download references

Funding

This work is supported by the National Natural Science Foundation of China under grant nos. 61762014 and 61762012, the Science and Technology Project of Guangxi under grant nos. 2018JJA170083 and 2018JJA170089.

Author information

Authors and Affiliations

  1. College of Electronics Engineering, Guangxi Normal University, 15 Yucai Road, Guilin, 541004, China

    HaiYing Xia, LingYu Wu, Yang Lan, HaiSheng Li & ShuXiang Song

Authors
  1. HaiYing Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. LingYu Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yang Lan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. HaiSheng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. ShuXiang Song
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to ShuXiang Song.

Ethics declarations

Ethics approval

This article does not contain any studies with human participants or animals performed by any of the authors. Important note: Informed consent was obtained from all individual participants included in the study.

Conflict of Interests

All authors of this manuscript declare that they have no conflict of interest.

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

Xia, H., Wu, L., Lan, Y. et al. HRNet:A hierarchical recurrent convolution neural network for retinal vessel segmentation. Multimed Tools Appl 81, 39829–39851 (2022). https://doi.org/10.1007/s11042-022-12696-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12696-4

Keywords

Search

Navigation