Segmentation of retinal image vessels based on fully convolutional network with depthwise separable convolution and channel weighting_Journal of Biomedical Engineering

Authors：

GENG Lei ^1,2 , QIU Ling ^1,2 ,  WU Jun ^1,2 , XIAO Zhitao ^1,2 , ZHANG Fang ^1,2

1. Tianjin Polytechnic University, School of Electronics and Information Engineering, Tianjin 300387, P.R.China;
2. Tianjin Key Laboratory of Optoelectronic Detection and Systems, Tianjin 300387, P.R.China;

Corresponding author：

WU Jun, Email: zhenkongwujun@163.com

Keywords：

segmentation of retinal blood vessels; fully convolutional network; depthwise separable convolution; channel weighting

DOI：

10.7507/1001-5515.201801054

Video：

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Diseases such as diabetes and hypertension can lead to change the shape of the retinal blood vessels. Segmentation of fundus images is a key step in the process of quantitative analysis of the disease, which is instructive in the analysis and diagnosis of clinical diseases. In this paper, a method for the segmentation of retinal image vessels based on fully convolutional network (FCN) with depthwise separable convolution and channel weighting is presented. Firstly, CLAHE and Gamma correction of the green channel of the fundus image are used to enhance the contrast. Then, in order to adapt to network training, the enhanced image is divided into patches to expand the data. Finally, the depthwise separable convolution instead of the standard convolution method is used to increase the network width. Meanwhile, the channel weighting module is introduced to explicitly model the relationship between the characteristic channels in order to improve the distinguishability of the features. The combination of them is applied to the FCN and the results of expert manual identification are used to supervise the experiment on the DRIVE database. The results show that the segmentation accuracy of the proposed method in DRIVE database reached 0.963 0 and AUC reached 0.983 1. The segmentation accuracy in STARE database reached 0.962 0 and AUC achieved 0.983 0. To some extent, the proposed method has better feature resolution and better segmentation performance.

Citation： GENG Lei, QIU Ling, WU Jun, XIAO Zhitao, ZHANG Fang. Segmentation of retinal image vessels based on fully convolutional network with depthwise separable convolution and channel weighting. Journal of Biomedical Engineering, 2019, 36(1): 107-115. doi: 10.7507/1001-5515.201801054 Copy

1.	Abràmoff M D, Folk J C, Han D P, et al. Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmol, 2013, 131(3): 351-357.
2.	周琳. 眼底图像中血管分割技术研究. 南京: 南京航空航天大学, 2011.
3.	姚畅. 眼底图像分割方法的研究及其应用. 北京: 北京交通大学, 2009.
4.	Ganjee R, Azmi R, Gholizadeh B. An improved retinal vessel segmentation method based on high level features for pathological images. J Med Syst, 2014, 38(9): 108-117.
5.	Kirbas C, Quek F. A review of vessel extraction techniques and algorithms. ACM Computing Surveys, 2004, 36(2): 81-121.
6.	Lam B S, Gao Yongsheng, Liew A W. General retinal vessel segmentation using regularization-based multiconcavity modeling. IEEE Trans Med Imaging, 2010, 29(7): 1369-1381.
7.	Nayebifar B, Abrishami Moghaddam H. A novel method for retinal vessel tracking using particle filters. Comput Biol Med, 2013, 43(5): 541-548.
8.	Azzopardi G, Strisciuglio N, Vento M, et al. Trainable COSFIRE filters for vessel delineation with application to retinal images. Med Image Anal, 2015, 19(1): 46-57.
9.	Fraz M M, Remagnino P, Hoppe A, et al. Blood vessel segmentation methodologies in retinal images--A survey. Comput Methods Programs Biomed, 2012, 108(1): 407-433.
10.	Karthika D, Marimuthu A. Retinal image analysis using contourlet transform and multistructure elements morphology by reconstruction// 2014 World Congress on Computing and Communication Technologies. Trichirappalli, India: IEEE, 2014, 9(8): 54-59.
11.	Ricci E, Perfetti R. Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans Med Imaging, 2007, 26(10): 1357-1365.
12.	Marin D, Aquino A, Gegundez-Arias M E, et al. A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans Med Imaging, 2011, 30(1): 146-158.
13.	Wang Shuangling, Yin Yilong, Cao Guibao, et al. Hierarchical retinal blood vessel segmentation based on feature and ensemble learning. Neurocomputing, 2015, 149(B): 708-717.
14.	Liskowski P, Krawiec K. Segmenting retinal blood vessels with deep neural networks. IEEE Trans Med Imaging, 2016, 35(11): 2369-2380.
15.	Fu Huazhu, Xu Yanwu, Wong D W K, et al. Retinal vessel segmentation via deep learning network and fully-connected conditional random fields// 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI). Prague, Czech Republic: IEEE, 2016: 698-701.
16.	Khalaf A F, Yassine I A, Fahmy A S. Convolutional neural networks for deep feature learning in retinal vessel segmentation// 2016 IEEE International Conference on Image Processing (ICIP). Phoenix, AZ, USA: IEEE, 2016: 385-388.
17.	Staal J, Abràmoff M D, Niemeijer M, et al. Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging, 2004, 23(4): 501-509.
18.	Ngo L, Han J H. Multi-level deep neural network for efficient segmentation of blood vessels in fundus images. Electron Lett, 2017, 53(16): 1096-1098.
19.	Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell, 2017, 39(4): 640-651.
20.	Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation// The 18th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Munich, Germany: MICCAI, 2015: 234-241.
21.	Chollet F. Xception: Deep learning with depthwise separable convolutions. arXiv preprint arXiv, 2016: 1610.02357.
22.	Hu J, Shen L, Sun G. Squeeze-and-Excitation Networks. arXiv preprint arXiv, 2017: 1709.01507.
23.	Hoover A, Kouznetsova V, Goldbaum M. Locating blood vessels in retinal images by piece-wise threshold probing of a matched filter response. IEEE Trans Med Imaging, 1998, 19(3): 931-935.
24.	Niemeijer M, Abràmoff M D, Ginneken B V. Publicly available retinal image data and the use of competitions to standardize algorithm performance comparison// IFMBE Proceedings. Berlin, Heidelberg: Springer, 2009, 25/11: 175-178.
25.	Soares J V, Leandro J J, Cesar Júnior R M, et al. Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans Med Imaging, 2006, 25(9): 1214-1222.
26.	Cheng Erkang, Du Liang, Wu Yi, et al. Discriminative vessel segmentation in retinal images by fusing context-aware hybrid features. Mach Vis Appl, 2014, 25(7): 1779-1792.
27.	Orlando J I, Blaschko M. Learning fully-connected CRFs for blood vessel segmentation in retinal images// International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2014: 634-641.

1. Abràmoff M D, Folk J C, Han D P, et al. Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmol, 2013, 131(3): 351-357.
2. 周琳. 眼底图像中血管分割技术研究. 南京: 南京航空航天大学, 2011.
3. 姚畅. 眼底图像分割方法的研究及其应用. 北京: 北京交通大学, 2009.
4. Ganjee R, Azmi R, Gholizadeh B. An improved retinal vessel segmentation method based on high level features for pathological images. J Med Syst, 2014, 38(9): 108-117.
5. Kirbas C, Quek F. A review of vessel extraction techniques and algorithms. ACM Computing Surveys, 2004, 36(2): 81-121.
6. Lam B S, Gao Yongsheng, Liew A W. General retinal vessel segmentation using regularization-based multiconcavity modeling. IEEE Trans Med Imaging, 2010, 29(7): 1369-1381.
7. Nayebifar B, Abrishami Moghaddam H. A novel method for retinal vessel tracking using particle filters. Comput Biol Med, 2013, 43(5): 541-548.
8. Azzopardi G, Strisciuglio N, Vento M, et al. Trainable COSFIRE filters for vessel delineation with application to retinal images. Med Image Anal, 2015, 19(1): 46-57.
9. Fraz M M, Remagnino P, Hoppe A, et al. Blood vessel segmentation methodologies in retinal images--A survey. Comput Methods Programs Biomed, 2012, 108(1): 407-433.
10. Karthika D, Marimuthu A. Retinal image analysis using contourlet transform and multistructure elements morphology by reconstruction// 2014 World Congress on Computing and Communication Technologies. Trichirappalli, India: IEEE, 2014, 9(8): 54-59.
11. Ricci E, Perfetti R. Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans Med Imaging, 2007, 26(10): 1357-1365.
12. Marin D, Aquino A, Gegundez-Arias M E, et al. A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans Med Imaging, 2011, 30(1): 146-158.
13. Wang Shuangling, Yin Yilong, Cao Guibao, et al. Hierarchical retinal blood vessel segmentation based on feature and ensemble learning. Neurocomputing, 2015, 149(B): 708-717.
14. Liskowski P, Krawiec K. Segmenting retinal blood vessels with deep neural networks. IEEE Trans Med Imaging, 2016, 35(11): 2369-2380.
15. Fu Huazhu, Xu Yanwu, Wong D W K, et al. Retinal vessel segmentation via deep learning network and fully-connected conditional random fields// 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI). Prague, Czech Republic: IEEE, 2016: 698-701.
16. Khalaf A F, Yassine I A, Fahmy A S. Convolutional neural networks for deep feature learning in retinal vessel segmentation// 2016 IEEE International Conference on Image Processing (ICIP). Phoenix, AZ, USA: IEEE, 2016: 385-388.
17. Staal J, Abràmoff M D, Niemeijer M, et al. Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging, 2004, 23(4): 501-509.
18. Ngo L, Han J H. Multi-level deep neural network for efficient segmentation of blood vessels in fundus images. Electron Lett, 2017, 53(16): 1096-1098.
19. Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell, 2017, 39(4): 640-651.
20. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation// The 18th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Munich, Germany: MICCAI, 2015: 234-241.
21. Chollet F. Xception: Deep learning with depthwise separable convolutions. arXiv preprint arXiv, 2016: 1610.02357.
22. Hu J, Shen L, Sun G. Squeeze-and-Excitation Networks. arXiv preprint arXiv, 2017: 1709.01507.
23. Hoover A, Kouznetsova V, Goldbaum M. Locating blood vessels in retinal images by piece-wise threshold probing of a matched filter response. IEEE Trans Med Imaging, 1998, 19(3): 931-935.
24. Niemeijer M, Abràmoff M D, Ginneken B V. Publicly available retinal image data and the use of competitions to standardize algorithm performance comparison// IFMBE Proceedings. Berlin, Heidelberg: Springer, 2009, 25/11: 175-178.
25. Soares J V, Leandro J J, Cesar Júnior R M, et al. Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans Med Imaging, 2006, 25(9): 1214-1222.
26. Cheng Erkang, Du Liang, Wu Yi, et al. Discriminative vessel segmentation in retinal images by fusing context-aware hybrid features. Mach Vis Appl, 2014, 25(7): 1779-1792.
27. Orlando J I, Blaschko M. Learning fully-connected CRFs for blood vessel segmentation in retinal images// International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2014: 634-641.

Journal of Biomedical Engineering

Segmentation of retinal image vessels based on fully convolutional network with depthwise separable convolution and channel weighting

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