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International Conference on Neural Information Processing

ICONIP 2020: Neural Information Processing pp 159–167Cite as

A Semantically Flexible Feature Fusion Network for Retinal Vessel Segmentation

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1332))

Abstract

The automatic detection of retinal blood vessels by computer aided techniques plays an important role in the diagnosis of diabetic retinopathy, glaucoma, and macular degeneration. In this paper we present a semantically flexible feature fusion network that employs residual skip connections between adjacent neurons to improve retinal vessel detection. This yields a method that can be trained employing residual learning. To illustrate the utility of our method for retinal blood vessel detection, we show results on two publicly available data sets, i.e. DRIVE and STARE. In our experimental evaluation we include widely used evaluation metrics and compare our results with those yielded by alternatives elsewhere in the literature. In our experiments, our method is quite competitive, delivering a margin of sensitivity and accuracy improvement as compared to the alternatives under consideration.

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Notes

  1. 1.

    The dataset is widely available at https://drive.grand-challenge.org/.

  2. 2.

    The dataset can be accessed at https://cecas.clemson.edu/~ahoover/stare/probing/index.html.

References

  1. Khawaja, A., Khan, T.M., Khan, M.A.U., Nawaz, S.J.: A multi-scale directional line detector for retinal vessel segmentation. Sensors 19(22), 4949 (2019)

    Article  Google Scholar 

  2. Khawaja, A., Khan, T.M., Naveed, K., Naqvi, S.S., Rehman, N.U., Junaid Nawaz, S.: An improved retinal vessel segmentation framework using frangi filter coupled with the probabilistic patch based denoiser. IEEE Access 7, 164344–164361 (2019)

    Article  Google Scholar 

  3. Klein, R., Klein, B.E., Moss, S.E.: Visual impairment in diabetes. Ophthalmology 91(1), 1–9 (1984)

    Article  Google Scholar 

  4. Soomro, T.A., Khan, T.M., Khan, M.A.U., Gao, J., Paul, M., Zheng, L.: Impact of ICA-based image enhancement technique on retinal blood vessels segmentation. IEEE Access 6, 3524–3538 (2018)

    Article  Google Scholar 

  5. Zhang, J., Li, H., Nie, Q., Cheng, L.: A retinal vessel boundary tracking method based on Bayesian theory and multi-scale line detection. Comput. Med. Imag. Graph. 38(6), 517–525 (2014)

    Article  Google Scholar 

  6. Memari, N., Saripan, M.I.B., Mashohor, S., Moghbel, M.: Retinal blood vessel segmentation by using matched filtering and fuzzy c-means clustering with integrated level set method for diabetic retinopathy assessment. J. Med. Biol. Eng. 1–19 (2018)

    Google Scholar 

  7. Almotiri, J., Elleithy, K., Elleithy, A.: Retinal vessels segmentation techniques and algorithms: a survey. Appl. Sci. 8, 01 (2018)

    Article  Google Scholar 

  8. Thakoor, K.A., Li, X., Tsamis, E., Sajda, P., Hood, D.C.: Enhancing the accuracy of glaucoma detection from oct probability maps using convolutional neural networks. In: International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2036–2040 (2019)

    Google Scholar 

  9. Zeng, X., Chen, H., Luo, Y., Ye, W.: Automated diabetic retinopathy detection based on binocular siamese-like convolutional neural network. IEEE Access 7, 30 744–30 753 (2019)

    Google Scholar 

  10. Muraoka, Y., et al.: Morphologic and functional changes in retinal vessels associated with branch retinal vein occlusion. Ophthalmology 120(1), 91–99 (2013)

    Article  Google Scholar 

  11. Cicinelli, M.V., et al.: Optical coherence tomography angiography in dry age-related macular degeneration. Surv. Ophthalmol. 63(2), 236–244 (2018)

    Article  Google Scholar 

  12. Traustason, S., Jensen, A.S., Arvidsson, H.S., Munch, I.C., Søndergaard, L., Larsen, M.: Retinal oxygen saturation in patients with systemic hypoxemia. Invest. Ophthalmol. Vis. Sci. 52(8), 5064 (2011)

    Article  Google Scholar 

  13. Jiang, Y., Tan, N., Peng, T.: Optic disc and cup segmentation based on deep convolutional generative adversarial networks. IEEE Access 7, 64 483–64 493 (2019)

    Google Scholar 

  14. Jiang, Y., Zhang, H., Tan, N., Chen, L.: Automatic retinal blood vessel segmentation based on fully convolutional neural networks. Symmetry 11, 1112 (2019)

    Google Scholar 

  15. Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856–1867 (2020)

    Article  Google Scholar 

  16. Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning (2017)

    Google Scholar 

  17. Ibtehaz, N., Rahman, M.S.: Multiresunet: rethinking the u-net architecture for multimodal biomedical image segmentation. Neural Netw. 121, 74–87 (2020)

    Article  Google Scholar 

  18. Staal, J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. Li, Q., Feng, B., Xie, L., Liang, P., Zhang, H., Wang, T.: A cross-modality learning approach for vessel segmentation in retinal images. IEEE Trans. Med. Imaging 35(1), 109–118 (2016)

    Article  Google Scholar 

  21. Orlando, J.I., Prokofyeva, E., Blaschko, M.B.: A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images. IEEE Trans. Biomed. Eng. 64(1), 16–27 (2016)

    Article  Google Scholar 

  22. Dasgupta, A., Singh, S.: A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation. In: International Symposium on Biomedical Imaging, pp. 248–251 (2017)

    Google Scholar 

  23. Yan, Z., Yang, X., Cheng, K.T.: Joint segment-level and pixel-wise losses for deep learning based retinal vessel segmentation. IEEE Trans. Biomed. Eng. 1 (2018)

    Google Scholar 

  24. Jiang, Y., Tan, N., Peng, T., Zhang, H.: Retinal vessels segmentation based on dilated multi-scale convolutional neural network. IEEE Access 7, 76 342–76 352 (2019)

    Google Scholar 

  25. Adapa, D., et al.: A supervised blood vessel segmentation technique for digital fundus images using zernike moment based features. PLOS ONE 15(3), 1–23 (2020)

    Google Scholar 

  26. Zhang, J., Dashtbozorg, B., Bekkers, E., Pluim, J.P.W., Duits, R., Romeny, B.M.: Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores. IEEE Trans. Med. Imaging 35(12), 2631–2644 (2016)

    Article  Google Scholar 

  27. Soomro, T.A., Afifi, A.J., Gao, J., Hellwich, O., Zheng, L., Paul, M.: Strided fully convolutional neural network for boosting the sensitivity of retinal blood vessels segmentation. Expert Syst. Appl. 134, 36–52 (2019)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of IT, Deakin University, Waurn Ponds, VIC, 3216, Australia

    Tariq M. Khan & Antonio Robles-Kelly

  2. Department of Electrical and Computer Engineering, COSMATS University, Islamabad, Pakistan

    Syed S. Naqvi

Authors
  1. Tariq M. Khan
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  2. Antonio Robles-Kelly
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  3. Syed S. Naqvi
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Corresponding author

Correspondence to Tariq M. Khan .

Editor information

Editors and Affiliations

  1. Department of AI, Ping An Life, Shenzhen, China

    Haiqin Yang

  2. Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Kitsuchart Pasupa

  3. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi-Sing Leung

  4. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong

    James T. Kwok

  5. School of Information Technology, King Mongkut's University of Technology Thonburi, Bangkok, Thailand

    Jonathan H. Chan

  6. The Chinese University of Hong Kong, New Territories, Hong Kong

    Irwin King

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Khan, T.M., Robles-Kelly, A., Naqvi, S.S. (2020). A Semantically Flexible Feature Fusion Network for Retinal Vessel Segmentation. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_18

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  • DOI: https://doi.org/10.1007/978-3-030-63820-7_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63819-1

  • Online ISBN: 978-3-030-63820-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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