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Staircase-Net: a deep learning based architecture for retinal blood vessel segmentation

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Abstract

The retina is one of the most metabolically active tissues in the human body. The retinal vessels provide blood to the inner retinal neurons. The retinal blood vessels are affected by diseases such as Hypertensive retinopathy and Diabetic retinopathy. The early diagnosis prevents the patients from blindness and fatality in some cases. Thus, examining the retinal blood vessels becomes an important work of an ophthalmologist. Thus, automated retinal blood vessel segmentation aids the ophthalmologist and makes their work easier. In this paper, a supervised Convolutional Neural Network (CNN) is suggested that enhances the performance of retinal blood vessel segmentation. Three publicly available datasets are used: STARE, DRIVE, and CHASE_DB1. A novel model, ’Staircase-Net,’ is proposed, which has a series of up-sampling and down-sampling processes for feature extraction (extracting the thick and thin blood vessel features, respectively). The images in the datasets undergo a series of transformations in the preprocessing steps. The evaluation metrics considered are specificity, accuracy, sensitivity, and area under the curve. Finally, the proposed model results are compared with the state-of-the-art techniques.

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References

  1. Eladawi N, Elmogy M, Khalifa F, Ghazal M, Ghazi N, Aboelfetouh A, Riad A, Sandhu H, Schaal S and El-Baz A 2018 Early diabetic retinopathy diagnosis based on local retinal blood vessel analysis in optical coherence tomography angiography (octa) images. Med. Phys. 45(10): 4582–4599

    Article  Google Scholar 

  2. Eladawi N, ElTanboly A, Elmogy M, Ghazal M, Fraiwan L, Aboelfetouh A, Riad A, Keynton R, El-Azab M and Schaal S et al 2019 Diabetic retinopathy early detection based on oct and octa feature fusion. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), IEEE, pp 587–591

  3. Feng Z, Yang J and Yao L 2017 Patch-based fully convolutional neural network with skip connections for retinal blood vessel segmentation. In: 2017 IEEE International Conference on Image Processing (ICIP), IEEE, pp 1742–1746

  4. Li Q, Feng B, Xie L, Liang P, Zhang H and Wang T 2015 A cross-modality learning approach for vessel segmentation in retinal images. IEEE Trans. Med. Imaging 35(1): 109–118

    Article  Google Scholar 

  5. Wang S, Yin Y, Cao G, Wei B, Zheng Y and Yang G 2015 Hierarchical retinal blood vessel segmentation based on feature and ensemble learning. Neurocomputing 149: 708–717

    Article  Google Scholar 

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

    Article  Google Scholar 

  7. Guo Y, Budak Ü and Şengür A 2018 A novel retinal vessel detection approach based on multiple deep convolution neural networks. Comput. Methods Programs Biomed. 167: 43–48

    Article  Google Scholar 

  8. Wang X and Jiang X 2019 Retinal vessel segmentation by a divide-and-conquer funnel-structured classification framework. Signal Process. 165: 104–114

    Article  Google Scholar 

  9. Jiang Z, Zhang H, Wang Y and Ko S B 2018 Retinal blood vessel segmentation using fully convolutional network with transfer learning. Comput. Med. Imaging Gr. 68: 1–15

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. Hoover A, Kouznetsova V and 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 

  12. Owen C G, Rudnicka A R, Mullen R, Barman S A, Monekosso D, Whincup P H, Ng J and Paterson C 2009 Measuring retinal vessel tortuosity in 10-year-old children: validation of the computer-assisted image analysis of the retina (caiar) program. Invest. Ophthalmol. Vis. Sci. 50(5): 2004–2010

    Article  Google Scholar 

  13. Budai A, Bock R, Maier A, Hornegger J and Michelson G 2013 Robust vessel segmentation in fundus images. Int. J. Biomed. Imaging

  14. Sathananthavathi V and Indumathi G 2021 Encoder enhanced atrous (eea) unet architecture for retinal blood vessel segmentation. Cognit. Syst. Res. 67: 84–95

    Article  Google Scholar 

  15. Balasubramanian K and Ananthamoorthy N 2021 Robust retinal blood vessel segmentation using convolutional neural network and support vector machine. J. Ambient Intell. Hum. Comput. 12(3): 3559–3569

    Article  Google Scholar 

  16. Boudegga H, Elloumi Y, Akil M, Bedoui M H, Kachouri R and Abdallah AB 2021 Fast and efficient retinal blood vessel segmentation method based on deep learning network. Comput. Med. Imaging Gr. 90: 101902

    Article  Google Scholar 

  17. Atli I and Gedik O S 2021 Sine-net: A fully convolutional deep learning architecture for retinal blood vessel segmentation. Eng. Sci. Technol. Int. J. 24(2): 271–283

    Google Scholar 

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

  19. Chollet F et al 2018 Keras: The python deep learning library. Astrophysics Source Code Library pp ascl–1806

  20. Kingma D P and Ba J 2014 Adam: A method for stochastic optimization. arXiv preprint arXiv: 14126980

  21. Gayathri S, Gopi V P and Palanisamy P 2021 Diabetic retinopathy classification based on multipath cnn and machine learning classifiers. In: Physical and Engineering Sciences in Medicine pp 1–15

  22. Gayathri S, Gopi V P and Palanisamy P 2020 Automated classification of diabetic retinopathy through reliable feature selection. Phys. Eng. Sci. Med. 43(3): 927–945

    Article  Google Scholar 

  23. Thomas A, Harikrishnan P, Krishna A K, Palanisamy P and Gopi V P 2021 A novel multiscale convolutional neural network based age-related macular degeneration detection using oct images. Biomed. Signal Process. Control 67: 102538

    Article  Google Scholar 

  24. Mo J and Zhang L 2017 Multi-level deep supervised networks for retinal vessel segmentation. Int. J. Comput. Assisted Radiol. Surg. 12(12): 2181–2193

    Article  Google Scholar 

  25. Feng S, Zhuo Z, Pan D and Tian Q 2020 Ccnet: A cross-connected convolutional network for segmenting retinal vessels using multi-scale features. Neurocomputing 392: 268–276

    Article  Google Scholar 

  26. Hu K, Zhang Z, Niu X, Zhang Y, Cao C, Xiao F and Gao X 2018 Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function. Neurocomputing 309: 179–191

    Article  Google Scholar 

  27. Tchinda B S, Tchiotsop D, Noubom M, Louis-Dorr V and Wolf D 2021 Retinal blood vessels segmentation using classical edge detection filters and the neural network. Inform. Med. Unlocked 23: 100521

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology, Tiruchirappalli, Tamilnadu, 620015, India

    SRIVARADHARAJAN SETHURAMAN & VARUN PALAKUZHIYIL GOPI

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  1. SRIVARADHARAJAN SETHURAMAN
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  2. VARUN PALAKUZHIYIL GOPI
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Correspondence to VARUN PALAKUZHIYIL GOPI.

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SETHURAMAN, S., PALAKUZHIYIL GOPI, V. Staircase-Net: a deep learning based architecture for retinal blood vessel segmentation. Sādhanā 47, 191 (2022). https://doi.org/10.1007/s12046-022-01936-w

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  • DOI: https://doi.org/10.1007/s12046-022-01936-w

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