Abstract

The segmentation and classification of retinal arterioles and venules play an important role in the diagnosis of various eye diseases and systemic diseases. The major challenges include complicated vessel structure, inhomogeneous illumination, and large background variation across subjects. In this study, we employ a fully convolutional network to simultaneously segment arterioles and venules directly from the retinal image, rather than using a vessel segmentation-arteriovenous classification strategy as reported in most literature. To simultaneously segment retinal arterioles and venules, we configured the fully convolutional network to allow true color image as input and multiple labels as output. A domain-specific loss function was designed to improve the overall performance. The proposed method was assessed extensively on public data sets and compared with the state-of-the-art methods in literature. The sensitivity and specificity of overall vessel segmentation on DRIVE is 0.944 and 0.955 with a misclassification rate of 10.3% and 9.6% for arteriole and venule, respectively. The proposed method outperformed the state-of-the-art methods and avoided possible error-propagation as in the segmentation-classification strategy. The proposed method was further validated on a new database consisting of retinal images of different qualities and diseases. The proposed method holds great potential for the diagnostics and screening of various eye diseases and systemic diseases.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Full Article  |  PDF Article
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References

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2017 (3)

X. Xu, W. Ding, M. D. Abràmoff, and R. Cao, “An improved arteriovenous classification method for the early diagnostics of various diseases in retinal image,” Comput. Methods Programs Biomed. 141, 3–9 (2017).
[Crossref] [PubMed]

E. Shelhamer, J. Long, and T. Darrell, “Fully convolutional networks for semantic segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017).
[Crossref] [PubMed]

S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017).
[Crossref] [PubMed]

2016 (5)

X. Xu, W. Ding, X. Wang, R. Cao, M. Zhang, P. Lv, and F. Xu, “Smartphone-based accurate analysis of retinal vasculature towards point-of-care diagnostics,” Sci. Rep. 6(1), 34603 (2016).
[Crossref] [PubMed]

J. Zhang, B. Dashtbozorg, E. Bekkers, J. P. W. Pluim, R. Duits, and B. M. Ter Haar Romeny, “Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores,” IEEE Trans. Med. Imaging 35(12), 2631–2644 (2016).
[Crossref] [PubMed]

Q. Li, B. Feng, L. Xie, P. Liang, H. Zhang, and T. Wang, “A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images,” IEEE Trans. Med. Imaging 35(1), 109–118 (2016).
[Crossref] [PubMed]

R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Region-Based Convolutional Networks for Accurate Object Detection and Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 38(1), 142–158 (2016).
[Crossref] [PubMed]

S. B. Seidelmann, B. Claggett, P. E. Bravo, A. Gupta, H. Farhad, B. E. Klein, R. Klein, M. Di Carli, and S. D. Solomon, “Retinal vessel calibers in predicting long-term cardiovascular outcomes: the atherosclerosis risk in communities study,” Circulation 134(18), 1328–1338 (2016).
[Crossref] [PubMed]

2015 (5)

Q. Hu, M. D. Abràmoff, and M. K. Garvin, “Automated construction of arterial and venous trees in retinal images,” J. Med. Imaging (Bellingham) 2(4), 044001 (2015).
[Crossref] [PubMed]

R. Estrada, M. J. Allingham, P. S. Mettu, S. W. Cousins, C. Tomasi, and S. Farsiu, “Retinal Artery-Vein Classification via Topology Estimation,” IEEE Trans. Med. Imaging 34(12), 2518–2534 (2015).
[Crossref] [PubMed]

K. He, X. Zhang, S. Ren, and J. Sun, “Spatial Pyramid pooling in deep convolutional networks for visual recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015).
[Crossref] [PubMed]

S. Roychowdhury, D. D. Koozekanani, and K. K. Parhi, “Iterative Vessel Segmentation of Fundus Images,” IEEE Trans. Biomed. Eng. 62(7), 1738–1749 (2015).
[Crossref] [PubMed]

S. Wang, Y. Yin, G. Cao, B. Wei, Y. Zheng, and G. Yang, “Hierarchical retinal blood vessel segmentation based on feature and ensemble learning,” Neurocomputing 149, 708–717 (2015).
[Crossref]

2014 (1)

B. Dashtbozorg, A. M. Mendonça, and A. Campilho, “An Automatic Graph-Based Approach for Artery/Vein Classification in Retinal Images,” IEEE Trans. Image Process. 23(3), 1073–1083 (2014).
[Crossref] [PubMed]

2013 (1)

S. Vázquez, B. Cancela, N. Barreira, M. G. Penedo, M. Rodríguez-Blanco, M. P. Seijo, G. C. de Tuero, M. A. Barceló, and M. Saez, “Improving retinal artery and vein classification by means of a minimal path approach,” Mach. Vis. Appl. 24(5), 919–930 (2013).
[Crossref]

2012 (1)

M. Saez, S. González-Vázquez, M. González-Penedo, M. A. Barceló, M. Pena-Seijo, G. Coll de Tuero, and A. Pose-Reino, “Development of an automated system to classify retinal vessels into arteries and veins,” Comput. Methods Programs Biomed. 108(1), 367–376 (2012).
[Crossref] [PubMed]

2011 (4)

M. Niemeijer, X. Xu, A. V. Dumitrescu, P. Gupta, B. van Ginneken, J. C. Folk, and M. D. Abramoff, “Automated measurement of the arteriolar-to-venular width ratio in digital color fundus photographs,” IEEE Trans. Med. Imaging 30(11), 1941–1950 (2011).
[Crossref] [PubMed]

D. Marin, A. Aquino, M. E. Gegundez-Arias, and J. M. Bravo, “A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features,” IEEE Trans. Med. Imaging 30(1), 146–158 (2011).
[Crossref] [PubMed]

M. S. Miri and A. Mahloojifar, “Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction,” IEEE Trans. Biomed. Eng. 58(5), 1183–1192 (2011).
[Crossref] [PubMed]

X. Xu, M. Niemeijer, Q. Song, M. Sonka, M. K. Garvin, J. M. Reinhardt, and M. D. Abràmoff, “Vessel boundary delineation on fundus images using graph-based approach,” IEEE Trans. Med. Imaging 30(6), 1184–1191 (2011).
[Crossref] [PubMed]

2010 (1)

M. D. Abràmoff, M. K. Garvin, and M. Sonka, “Retinal imaging and image analysis,” IEEE Rev. Biomed. Eng. 3, 169–208 (2010).
[Crossref] [PubMed]

2009 (3)

K. Rothaus, X. Jiang, and P. Rhiem, “Separation of the retinal vascular graph in arteries and veins based upon structural knowledge,” Image Vis. Comput. 27(7), 864–875 (2009).
[Crossref]

J. Grauslund, L. Hodgson, R. Kawasaki, A. Green, A. K. Sjølie, and T. Y. Wong, “Retinal vessel calibre and micro- and macrovascular complications in type 1 diabetes,” Diabetologia 52(10), 2213–2217 (2009).
[Crossref] [PubMed]

D. A. De Silva, G. Liew, M.-C. Wong, H.-M. Chang, C. Chen, J. J. Wang, M. L. Baker, P. J. Hand, E. Rochtchina, E. Y. Liu, P. Mitchell, R. I. Lindley, and T. Y. Wong, “Retinal vascular caliber and extracranial carotid disease in patients with acute ischemic stroke,” The Multi-Centre Retinal Stroke (MCRS) study 40(12), 3695–3699 (2009).
[Crossref] [PubMed]

2007 (2)

E. Ricci and R. Perfetti, “Retinal blood vessel segmentation using line operators and support vector classification,” IEEE Trans. Med. Imaging 26(10), 1357–1365 (2007).
[Crossref] [PubMed]

H. Narasimha-Iyer, J. M. Beach, B. Khoobehi, and B. Roysam, “Automatic identification of retinal arteries and veins from dual-wavelength images using structural and functional features,” IEEE Trans. Biomed. Eng. 54(8), 1427–1435 (2007).
[Crossref] [PubMed]

2005 (1)

R. F. Gariano and T. W. Gardner, “Retinal angiogenesis in development and disease,” Nature 438(7070), 960–966 (2005).
[Crossref] [PubMed]

2004 (1)

J. Staal, M. D. Abràmoff, M. Niemeijer, M. A. Viergever, and B. van Ginneken, “Ridge-based vessel segmentation in color images of the retina,” IEEE Trans. Med. Imaging 23(4), 501–509 (2004).
[Crossref] [PubMed]

Abramoff, M. D.

M. Niemeijer, X. Xu, A. V. Dumitrescu, P. Gupta, B. van Ginneken, J. C. Folk, and M. D. Abramoff, “Automated measurement of the arteriolar-to-venular width ratio in digital color fundus photographs,” IEEE Trans. Med. Imaging 30(11), 1941–1950 (2011).
[Crossref] [PubMed]

Abràmoff, M. D.

X. Xu, W. Ding, M. D. Abràmoff, and R. Cao, “An improved arteriovenous classification method for the early diagnostics of various diseases in retinal image,” Comput. Methods Programs Biomed. 141, 3–9 (2017).
[Crossref] [PubMed]

Q. Hu, M. D. Abràmoff, and M. K. Garvin, “Automated construction of arterial and venous trees in retinal images,” J. Med. Imaging (Bellingham) 2(4), 044001 (2015).
[Crossref] [PubMed]

X. Xu, M. Niemeijer, Q. Song, M. Sonka, M. K. Garvin, J. M. Reinhardt, and M. D. Abràmoff, “Vessel boundary delineation on fundus images using graph-based approach,” IEEE Trans. Med. Imaging 30(6), 1184–1191 (2011).
[Crossref] [PubMed]

M. D. Abràmoff, M. K. Garvin, and M. Sonka, “Retinal imaging and image analysis,” IEEE Rev. Biomed. Eng. 3, 169–208 (2010).
[Crossref] [PubMed]

J. Staal, M. D. Abràmoff, M. Niemeijer, M. A. Viergever, and B. van Ginneken, “Ridge-based vessel segmentation in color images of the retina,” IEEE Trans. Med. Imaging 23(4), 501–509 (2004).
[Crossref] [PubMed]

Allingham, M. J.

R. Estrada, M. J. Allingham, P. S. Mettu, S. W. Cousins, C. Tomasi, and S. Farsiu, “Retinal Artery-Vein Classification via Topology Estimation,” IEEE Trans. Med. Imaging 34(12), 2518–2534 (2015).
[Crossref] [PubMed]

Aquino, A.

D. Marin, A. Aquino, M. E. Gegundez-Arias, and J. M. Bravo, “A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features,” IEEE Trans. Med. Imaging 30(1), 146–158 (2011).
[Crossref] [PubMed]

Baker, M. L.

D. A. De Silva, G. Liew, M.-C. Wong, H.-M. Chang, C. Chen, J. J. Wang, M. L. Baker, P. J. Hand, E. Rochtchina, E. Y. Liu, P. Mitchell, R. I. Lindley, and T. Y. Wong, “Retinal vascular caliber and extracranial carotid disease in patients with acute ischemic stroke,” The Multi-Centre Retinal Stroke (MCRS) study 40(12), 3695–3699 (2009).
[Crossref] [PubMed]

Barceló, M. A.

S. Vázquez, B. Cancela, N. Barreira, M. G. Penedo, M. Rodríguez-Blanco, M. P. Seijo, G. C. de Tuero, M. A. Barceló, and M. Saez, “Improving retinal artery and vein classification by means of a minimal path approach,” Mach. Vis. Appl. 24(5), 919–930 (2013).
[Crossref]

M. Saez, S. González-Vázquez, M. González-Penedo, M. A. Barceló, M. Pena-Seijo, G. Coll de Tuero, and A. Pose-Reino, “Development of an automated system to classify retinal vessels into arteries and veins,” Comput. Methods Programs Biomed. 108(1), 367–376 (2012).
[Crossref] [PubMed]

Barreira, N.

S. Vázquez, B. Cancela, N. Barreira, M. G. Penedo, M. Rodríguez-Blanco, M. P. Seijo, G. C. de Tuero, M. A. Barceló, and M. Saez, “Improving retinal artery and vein classification by means of a minimal path approach,” Mach. Vis. Appl. 24(5), 919–930 (2013).
[Crossref]

Beach, J. M.

H. Narasimha-Iyer, J. M. Beach, B. Khoobehi, and B. Roysam, “Automatic identification of retinal arteries and veins from dual-wavelength images using structural and functional features,” IEEE Trans. Biomed. Eng. 54(8), 1427–1435 (2007).
[Crossref] [PubMed]

Bekkers, E.

J. Zhang, B. Dashtbozorg, E. Bekkers, J. P. W. Pluim, R. Duits, and B. M. Ter Haar Romeny, “Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores,” IEEE Trans. Med. Imaging 35(12), 2631–2644 (2016).
[Crossref] [PubMed]

Bravo, J. M.

D. Marin, A. Aquino, M. E. Gegundez-Arias, and J. M. Bravo, “A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features,” IEEE Trans. Med. Imaging 30(1), 146–158 (2011).
[Crossref] [PubMed]

Bravo, P. E.

S. B. Seidelmann, B. Claggett, P. E. Bravo, A. Gupta, H. Farhad, B. E. Klein, R. Klein, M. Di Carli, and S. D. Solomon, “Retinal vessel calibers in predicting long-term cardiovascular outcomes: the atherosclerosis risk in communities study,” Circulation 134(18), 1328–1338 (2016).
[Crossref] [PubMed]

Brox, T.

O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015), 234–241.
[Crossref]

Campilho, A.

B. Dashtbozorg, A. M. Mendonça, and A. Campilho, “An Automatic Graph-Based Approach for Artery/Vein Classification in Retinal Images,” IEEE Trans. Image Process. 23(3), 1073–1083 (2014).
[Crossref] [PubMed]

Cancela, B.

S. Vázquez, B. Cancela, N. Barreira, M. G. Penedo, M. Rodríguez-Blanco, M. P. Seijo, G. C. de Tuero, M. A. Barceló, and M. Saez, “Improving retinal artery and vein classification by means of a minimal path approach,” Mach. Vis. Appl. 24(5), 919–930 (2013).
[Crossref]

Cao, G.

S. Wang, Y. Yin, G. Cao, B. Wei, Y. Zheng, and G. Yang, “Hierarchical retinal blood vessel segmentation based on feature and ensemble learning,” Neurocomputing 149, 708–717 (2015).
[Crossref]

Cao, R.

X. Xu, W. Ding, M. D. Abràmoff, and R. Cao, “An improved arteriovenous classification method for the early diagnostics of various diseases in retinal image,” Comput. Methods Programs Biomed. 141, 3–9 (2017).
[Crossref] [PubMed]

X. Xu, W. Ding, X. Wang, R. Cao, M. Zhang, P. Lv, and F. Xu, “Smartphone-based accurate analysis of retinal vasculature towards point-of-care diagnostics,” Sci. Rep. 6(1), 34603 (2016).
[Crossref] [PubMed]

Chang, H.-M.

D. A. De Silva, G. Liew, M.-C. Wong, H.-M. Chang, C. Chen, J. J. Wang, M. L. Baker, P. J. Hand, E. Rochtchina, E. Y. Liu, P. Mitchell, R. I. Lindley, and T. Y. Wong, “Retinal vascular caliber and extracranial carotid disease in patients with acute ischemic stroke,” The Multi-Centre Retinal Stroke (MCRS) study 40(12), 3695–3699 (2009).
[Crossref] [PubMed]

Chen, C.

D. A. De Silva, G. Liew, M.-C. Wong, H.-M. Chang, C. Chen, J. J. Wang, M. L. Baker, P. J. Hand, E. Rochtchina, E. Y. Liu, P. Mitchell, R. I. Lindley, and T. Y. Wong, “Retinal vascular caliber and extracranial carotid disease in patients with acute ischemic stroke,” The Multi-Centre Retinal Stroke (MCRS) study 40(12), 3695–3699 (2009).
[Crossref] [PubMed]

Claggett, B.

S. B. Seidelmann, B. Claggett, P. E. Bravo, A. Gupta, H. Farhad, B. E. Klein, R. Klein, M. Di Carli, and S. D. Solomon, “Retinal vessel calibers in predicting long-term cardiovascular outcomes: the atherosclerosis risk in communities study,” Circulation 134(18), 1328–1338 (2016).
[Crossref] [PubMed]

Coll de Tuero, G.

M. Saez, S. González-Vázquez, M. González-Penedo, M. A. Barceló, M. Pena-Seijo, G. Coll de Tuero, and A. Pose-Reino, “Development of an automated system to classify retinal vessels into arteries and veins,” Comput. Methods Programs Biomed. 108(1), 367–376 (2012).
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Cousins, S. W.

R. Estrada, M. J. Allingham, P. S. Mettu, S. W. Cousins, C. Tomasi, and S. Farsiu, “Retinal Artery-Vein Classification via Topology Estimation,” IEEE Trans. Med. Imaging 34(12), 2518–2534 (2015).
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E. Shelhamer, J. Long, and T. Darrell, “Fully convolutional networks for semantic segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017).
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R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Region-Based Convolutional Networks for Accurate Object Detection and Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 38(1), 142–158 (2016).
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Dashtbozorg, B.

J. Zhang, B. Dashtbozorg, E. Bekkers, J. P. W. Pluim, R. Duits, and B. M. Ter Haar Romeny, “Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores,” IEEE Trans. Med. Imaging 35(12), 2631–2644 (2016).
[Crossref] [PubMed]

B. Dashtbozorg, A. M. Mendonça, and A. Campilho, “An Automatic Graph-Based Approach for Artery/Vein Classification in Retinal Images,” IEEE Trans. Image Process. 23(3), 1073–1083 (2014).
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De Silva, D. A.

D. A. De Silva, G. Liew, M.-C. Wong, H.-M. Chang, C. Chen, J. J. Wang, M. L. Baker, P. J. Hand, E. Rochtchina, E. Y. Liu, P. Mitchell, R. I. Lindley, and T. Y. Wong, “Retinal vascular caliber and extracranial carotid disease in patients with acute ischemic stroke,” The Multi-Centre Retinal Stroke (MCRS) study 40(12), 3695–3699 (2009).
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S. Vázquez, B. Cancela, N. Barreira, M. G. Penedo, M. Rodríguez-Blanco, M. P. Seijo, G. C. de Tuero, M. A. Barceló, and M. Saez, “Improving retinal artery and vein classification by means of a minimal path approach,” Mach. Vis. Appl. 24(5), 919–930 (2013).
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Di Carli, M.

S. B. Seidelmann, B. Claggett, P. E. Bravo, A. Gupta, H. Farhad, B. E. Klein, R. Klein, M. Di Carli, and S. D. Solomon, “Retinal vessel calibers in predicting long-term cardiovascular outcomes: the atherosclerosis risk in communities study,” Circulation 134(18), 1328–1338 (2016).
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Ding, W.

X. Xu, W. Ding, M. D. Abràmoff, and R. Cao, “An improved arteriovenous classification method for the early diagnostics of various diseases in retinal image,” Comput. Methods Programs Biomed. 141, 3–9 (2017).
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X. Xu, W. Ding, X. Wang, R. Cao, M. Zhang, P. Lv, and F. Xu, “Smartphone-based accurate analysis of retinal vasculature towards point-of-care diagnostics,” Sci. Rep. 6(1), 34603 (2016).
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R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Region-Based Convolutional Networks for Accurate Object Detection and Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 38(1), 142–158 (2016).
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J. Zhang, B. Dashtbozorg, E. Bekkers, J. P. W. Pluim, R. Duits, and B. M. Ter Haar Romeny, “Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores,” IEEE Trans. Med. Imaging 35(12), 2631–2644 (2016).
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M. Niemeijer, X. Xu, A. V. Dumitrescu, P. Gupta, B. van Ginneken, J. C. Folk, and M. D. Abramoff, “Automated measurement of the arteriolar-to-venular width ratio in digital color fundus photographs,” IEEE Trans. Med. Imaging 30(11), 1941–1950 (2011).
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R. Estrada, M. J. Allingham, P. S. Mettu, S. W. Cousins, C. Tomasi, and S. Farsiu, “Retinal Artery-Vein Classification via Topology Estimation,” IEEE Trans. Med. Imaging 34(12), 2518–2534 (2015).
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Farhad, H.

S. B. Seidelmann, B. Claggett, P. E. Bravo, A. Gupta, H. Farhad, B. E. Klein, R. Klein, M. Di Carli, and S. D. Solomon, “Retinal vessel calibers in predicting long-term cardiovascular outcomes: the atherosclerosis risk in communities study,” Circulation 134(18), 1328–1338 (2016).
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Farsiu, S.

R. Estrada, M. J. Allingham, P. S. Mettu, S. W. Cousins, C. Tomasi, and S. Farsiu, “Retinal Artery-Vein Classification via Topology Estimation,” IEEE Trans. Med. Imaging 34(12), 2518–2534 (2015).
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Q. Li, B. Feng, L. Xie, P. Liang, H. Zhang, and T. Wang, “A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images,” IEEE Trans. Med. Imaging 35(1), 109–118 (2016).
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O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015), 234–241.
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M. Niemeijer, X. Xu, A. V. Dumitrescu, P. Gupta, B. van Ginneken, J. C. Folk, and M. D. Abramoff, “Automated measurement of the arteriolar-to-venular width ratio in digital color fundus photographs,” IEEE Trans. Med. Imaging 30(11), 1941–1950 (2011).
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Gariano, R. F.

R. F. Gariano and T. W. Gardner, “Retinal angiogenesis in development and disease,” Nature 438(7070), 960–966 (2005).
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Q. Hu, M. D. Abràmoff, and M. K. Garvin, “Automated construction of arterial and venous trees in retinal images,” J. Med. Imaging (Bellingham) 2(4), 044001 (2015).
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X. Xu, M. Niemeijer, Q. Song, M. Sonka, M. K. Garvin, J. M. Reinhardt, and M. D. Abràmoff, “Vessel boundary delineation on fundus images using graph-based approach,” IEEE Trans. Med. Imaging 30(6), 1184–1191 (2011).
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M. D. Abràmoff, M. K. Garvin, and M. Sonka, “Retinal imaging and image analysis,” IEEE Rev. Biomed. Eng. 3, 169–208 (2010).
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Gegundez-Arias, M. E.

D. Marin, A. Aquino, M. E. Gegundez-Arias, and J. M. Bravo, “A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features,” IEEE Trans. Med. Imaging 30(1), 146–158 (2011).
[Crossref] [PubMed]

Girshick, R.

S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017).
[Crossref] [PubMed]

R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Region-Based Convolutional Networks for Accurate Object Detection and Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 38(1), 142–158 (2016).
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González-Penedo, M.

M. Saez, S. González-Vázquez, M. González-Penedo, M. A. Barceló, M. Pena-Seijo, G. Coll de Tuero, and A. Pose-Reino, “Development of an automated system to classify retinal vessels into arteries and veins,” Comput. Methods Programs Biomed. 108(1), 367–376 (2012).
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González-Vázquez, S.

M. Saez, S. González-Vázquez, M. González-Penedo, M. A. Barceló, M. Pena-Seijo, G. Coll de Tuero, and A. Pose-Reino, “Development of an automated system to classify retinal vessels into arteries and veins,” Comput. Methods Programs Biomed. 108(1), 367–376 (2012).
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J. Grauslund, L. Hodgson, R. Kawasaki, A. Green, A. K. Sjølie, and T. Y. Wong, “Retinal vessel calibre and micro- and macrovascular complications in type 1 diabetes,” Diabetologia 52(10), 2213–2217 (2009).
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J. Grauslund, L. Hodgson, R. Kawasaki, A. Green, A. K. Sjølie, and T. Y. Wong, “Retinal vessel calibre and micro- and macrovascular complications in type 1 diabetes,” Diabetologia 52(10), 2213–2217 (2009).
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Gupta, A.

S. B. Seidelmann, B. Claggett, P. E. Bravo, A. Gupta, H. Farhad, B. E. Klein, R. Klein, M. Di Carli, and S. D. Solomon, “Retinal vessel calibers in predicting long-term cardiovascular outcomes: the atherosclerosis risk in communities study,” Circulation 134(18), 1328–1338 (2016).
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Gupta, P.

M. Niemeijer, X. Xu, A. V. Dumitrescu, P. Gupta, B. van Ginneken, J. C. Folk, and M. D. Abramoff, “Automated measurement of the arteriolar-to-venular width ratio in digital color fundus photographs,” IEEE Trans. Med. Imaging 30(11), 1941–1950 (2011).
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D. A. De Silva, G. Liew, M.-C. Wong, H.-M. Chang, C. Chen, J. J. Wang, M. L. Baker, P. J. Hand, E. Rochtchina, E. Y. Liu, P. Mitchell, R. I. Lindley, and T. Y. Wong, “Retinal vascular caliber and extracranial carotid disease in patients with acute ischemic stroke,” The Multi-Centre Retinal Stroke (MCRS) study 40(12), 3695–3699 (2009).
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S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017).
[Crossref] [PubMed]

K. He, X. Zhang, S. Ren, and J. Sun, “Spatial Pyramid pooling in deep convolutional networks for visual recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015).
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A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in International Conference on Neural Information Processing Systems, 2012), 1097–1105.

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J. Grauslund, L. Hodgson, R. Kawasaki, A. Green, A. K. Sjølie, and T. Y. Wong, “Retinal vessel calibre and micro- and macrovascular complications in type 1 diabetes,” Diabetologia 52(10), 2213–2217 (2009).
[Crossref] [PubMed]

Hu, Q.

Q. Hu, M. D. Abràmoff, and M. K. Garvin, “Automated construction of arterial and venous trees in retinal images,” J. Med. Imaging (Bellingham) 2(4), 044001 (2015).
[Crossref] [PubMed]

Jiang, X.

K. Rothaus, X. Jiang, and P. Rhiem, “Separation of the retinal vascular graph in arteries and veins based upon structural knowledge,” Image Vis. Comput. 27(7), 864–875 (2009).
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Kawasaki, R.

J. Grauslund, L. Hodgson, R. Kawasaki, A. Green, A. K. Sjølie, and T. Y. Wong, “Retinal vessel calibre and micro- and macrovascular complications in type 1 diabetes,” Diabetologia 52(10), 2213–2217 (2009).
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Khoobehi, B.

H. Narasimha-Iyer, J. M. Beach, B. Khoobehi, and B. Roysam, “Automatic identification of retinal arteries and veins from dual-wavelength images using structural and functional features,” IEEE Trans. Biomed. Eng. 54(8), 1427–1435 (2007).
[Crossref] [PubMed]

Klein, B. E.

S. B. Seidelmann, B. Claggett, P. E. Bravo, A. Gupta, H. Farhad, B. E. Klein, R. Klein, M. Di Carli, and S. D. Solomon, “Retinal vessel calibers in predicting long-term cardiovascular outcomes: the atherosclerosis risk in communities study,” Circulation 134(18), 1328–1338 (2016).
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Klein, R.

S. B. Seidelmann, B. Claggett, P. E. Bravo, A. Gupta, H. Farhad, B. E. Klein, R. Klein, M. Di Carli, and S. D. Solomon, “Retinal vessel calibers in predicting long-term cardiovascular outcomes: the atherosclerosis risk in communities study,” Circulation 134(18), 1328–1338 (2016).
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Koozekanani, D. D.

S. Roychowdhury, D. D. Koozekanani, and K. K. Parhi, “Iterative Vessel Segmentation of Fundus Images,” IEEE Trans. Biomed. Eng. 62(7), 1738–1749 (2015).
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A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in International Conference on Neural Information Processing Systems, 2012), 1097–1105.

Li, Q.

Q. Li, B. Feng, L. Xie, P. Liang, H. Zhang, and T. Wang, “A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images,” IEEE Trans. Med. Imaging 35(1), 109–118 (2016).
[Crossref] [PubMed]

Liang, P.

Q. Li, B. Feng, L. Xie, P. Liang, H. Zhang, and T. Wang, “A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images,” IEEE Trans. Med. Imaging 35(1), 109–118 (2016).
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D. A. De Silva, G. Liew, M.-C. Wong, H.-M. Chang, C. Chen, J. J. Wang, M. L. Baker, P. J. Hand, E. Rochtchina, E. Y. Liu, P. Mitchell, R. I. Lindley, and T. Y. Wong, “Retinal vascular caliber and extracranial carotid disease in patients with acute ischemic stroke,” The Multi-Centre Retinal Stroke (MCRS) study 40(12), 3695–3699 (2009).
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Lindley, R. I.

D. A. De Silva, G. Liew, M.-C. Wong, H.-M. Chang, C. Chen, J. J. Wang, M. L. Baker, P. J. Hand, E. Rochtchina, E. Y. Liu, P. Mitchell, R. I. Lindley, and T. Y. Wong, “Retinal vascular caliber and extracranial carotid disease in patients with acute ischemic stroke,” The Multi-Centre Retinal Stroke (MCRS) study 40(12), 3695–3699 (2009).
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Liu, E. Y.

D. A. De Silva, G. Liew, M.-C. Wong, H.-M. Chang, C. Chen, J. J. Wang, M. L. Baker, P. J. Hand, E. Rochtchina, E. Y. Liu, P. Mitchell, R. I. Lindley, and T. Y. Wong, “Retinal vascular caliber and extracranial carotid disease in patients with acute ischemic stroke,” The Multi-Centre Retinal Stroke (MCRS) study 40(12), 3695–3699 (2009).
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Long, J.

E. Shelhamer, J. Long, and T. Darrell, “Fully convolutional networks for semantic segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017).
[Crossref] [PubMed]

Lv, P.

X. Xu, W. Ding, X. Wang, R. Cao, M. Zhang, P. Lv, and F. Xu, “Smartphone-based accurate analysis of retinal vasculature towards point-of-care diagnostics,” Sci. Rep. 6(1), 34603 (2016).
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M. S. Miri and A. Mahloojifar, “Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction,” IEEE Trans. Biomed. Eng. 58(5), 1183–1192 (2011).
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Malik, J.

R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Region-Based Convolutional Networks for Accurate Object Detection and Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 38(1), 142–158 (2016).
[Crossref] [PubMed]

Marin, D.

D. Marin, A. Aquino, M. E. Gegundez-Arias, and J. M. Bravo, “A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features,” IEEE Trans. Med. Imaging 30(1), 146–158 (2011).
[Crossref] [PubMed]

Mendonça, A. M.

B. Dashtbozorg, A. M. Mendonça, and A. Campilho, “An Automatic Graph-Based Approach for Artery/Vein Classification in Retinal Images,” IEEE Trans. Image Process. 23(3), 1073–1083 (2014).
[Crossref] [PubMed]

Mettu, P. S.

R. Estrada, M. J. Allingham, P. S. Mettu, S. W. Cousins, C. Tomasi, and S. Farsiu, “Retinal Artery-Vein Classification via Topology Estimation,” IEEE Trans. Med. Imaging 34(12), 2518–2534 (2015).
[Crossref] [PubMed]

Miri, M. S.

M. S. Miri and A. Mahloojifar, “Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction,” IEEE Trans. Biomed. Eng. 58(5), 1183–1192 (2011).
[Crossref] [PubMed]

Mitchell, P.

D. A. De Silva, G. Liew, M.-C. Wong, H.-M. Chang, C. Chen, J. J. Wang, M. L. Baker, P. J. Hand, E. Rochtchina, E. Y. Liu, P. Mitchell, R. I. Lindley, and T. Y. Wong, “Retinal vascular caliber and extracranial carotid disease in patients with acute ischemic stroke,” The Multi-Centre Retinal Stroke (MCRS) study 40(12), 3695–3699 (2009).
[Crossref] [PubMed]

Narasimha-Iyer, H.

H. Narasimha-Iyer, J. M. Beach, B. Khoobehi, and B. Roysam, “Automatic identification of retinal arteries and veins from dual-wavelength images using structural and functional features,” IEEE Trans. Biomed. Eng. 54(8), 1427–1435 (2007).
[Crossref] [PubMed]

Niemeijer, M.

M. Niemeijer, X. Xu, A. V. Dumitrescu, P. Gupta, B. van Ginneken, J. C. Folk, and M. D. Abramoff, “Automated measurement of the arteriolar-to-venular width ratio in digital color fundus photographs,” IEEE Trans. Med. Imaging 30(11), 1941–1950 (2011).
[Crossref] [PubMed]

X. Xu, M. Niemeijer, Q. Song, M. Sonka, M. K. Garvin, J. M. Reinhardt, and M. D. Abràmoff, “Vessel boundary delineation on fundus images using graph-based approach,” IEEE Trans. Med. Imaging 30(6), 1184–1191 (2011).
[Crossref] [PubMed]

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

S. Roychowdhury, D. D. Koozekanani, and K. K. Parhi, “Iterative Vessel Segmentation of Fundus Images,” IEEE Trans. Biomed. Eng. 62(7), 1738–1749 (2015).
[Crossref] [PubMed]

Pena-Seijo, M.

M. Saez, S. González-Vázquez, M. González-Penedo, M. A. Barceló, M. Pena-Seijo, G. Coll de Tuero, and A. Pose-Reino, “Development of an automated system to classify retinal vessels into arteries and veins,” Comput. Methods Programs Biomed. 108(1), 367–376 (2012).
[Crossref] [PubMed]

Penedo, M. G.

S. Vázquez, B. Cancela, N. Barreira, M. G. Penedo, M. Rodríguez-Blanco, M. P. Seijo, G. C. de Tuero, M. A. Barceló, and M. Saez, “Improving retinal artery and vein classification by means of a minimal path approach,” Mach. Vis. Appl. 24(5), 919–930 (2013).
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Perfetti, R.

E. Ricci and R. Perfetti, “Retinal blood vessel segmentation using line operators and support vector classification,” IEEE Trans. Med. Imaging 26(10), 1357–1365 (2007).
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Pluim, J. P. W.

J. Zhang, B. Dashtbozorg, E. Bekkers, J. P. W. Pluim, R. Duits, and B. M. Ter Haar Romeny, “Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores,” IEEE Trans. Med. Imaging 35(12), 2631–2644 (2016).
[Crossref] [PubMed]

Pose-Reino, A.

M. Saez, S. González-Vázquez, M. González-Penedo, M. A. Barceló, M. Pena-Seijo, G. Coll de Tuero, and A. Pose-Reino, “Development of an automated system to classify retinal vessels into arteries and veins,” Comput. Methods Programs Biomed. 108(1), 367–376 (2012).
[Crossref] [PubMed]

Reinhardt, J. M.

X. Xu, M. Niemeijer, Q. Song, M. Sonka, M. K. Garvin, J. M. Reinhardt, and M. D. Abràmoff, “Vessel boundary delineation on fundus images using graph-based approach,” IEEE Trans. Med. Imaging 30(6), 1184–1191 (2011).
[Crossref] [PubMed]

Ren, S.

S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017).
[Crossref] [PubMed]

K. He, X. Zhang, S. Ren, and J. Sun, “Spatial Pyramid pooling in deep convolutional networks for visual recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015).
[Crossref] [PubMed]

Rhiem, P.

K. Rothaus, X. Jiang, and P. Rhiem, “Separation of the retinal vascular graph in arteries and veins based upon structural knowledge,” Image Vis. Comput. 27(7), 864–875 (2009).
[Crossref]

Ricci, E.

E. Ricci and R. Perfetti, “Retinal blood vessel segmentation using line operators and support vector classification,” IEEE Trans. Med. Imaging 26(10), 1357–1365 (2007).
[Crossref] [PubMed]

Rochtchina, E.

D. A. De Silva, G. Liew, M.-C. Wong, H.-M. Chang, C. Chen, J. J. Wang, M. L. Baker, P. J. Hand, E. Rochtchina, E. Y. Liu, P. Mitchell, R. I. Lindley, and T. Y. Wong, “Retinal vascular caliber and extracranial carotid disease in patients with acute ischemic stroke,” The Multi-Centre Retinal Stroke (MCRS) study 40(12), 3695–3699 (2009).
[Crossref] [PubMed]

Rodríguez-Blanco, M.

S. Vázquez, B. Cancela, N. Barreira, M. G. Penedo, M. Rodríguez-Blanco, M. P. Seijo, G. C. de Tuero, M. A. Barceló, and M. Saez, “Improving retinal artery and vein classification by means of a minimal path approach,” Mach. Vis. Appl. 24(5), 919–930 (2013).
[Crossref]

Ronneberger, O.

O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015), 234–241.
[Crossref]

Rothaus, K.

K. Rothaus, X. Jiang, and P. Rhiem, “Separation of the retinal vascular graph in arteries and veins based upon structural knowledge,” Image Vis. Comput. 27(7), 864–875 (2009).
[Crossref]

Roychowdhury, S.

S. Roychowdhury, D. D. Koozekanani, and K. K. Parhi, “Iterative Vessel Segmentation of Fundus Images,” IEEE Trans. Biomed. Eng. 62(7), 1738–1749 (2015).
[Crossref] [PubMed]

Roysam, B.

H. Narasimha-Iyer, J. M. Beach, B. Khoobehi, and B. Roysam, “Automatic identification of retinal arteries and veins from dual-wavelength images using structural and functional features,” IEEE Trans. Biomed. Eng. 54(8), 1427–1435 (2007).
[Crossref] [PubMed]

Saez, M.

S. Vázquez, B. Cancela, N. Barreira, M. G. Penedo, M. Rodríguez-Blanco, M. P. Seijo, G. C. de Tuero, M. A. Barceló, and M. Saez, “Improving retinal artery and vein classification by means of a minimal path approach,” Mach. Vis. Appl. 24(5), 919–930 (2013).
[Crossref]

M. Saez, S. González-Vázquez, M. González-Penedo, M. A. Barceló, M. Pena-Seijo, G. Coll de Tuero, and A. Pose-Reino, “Development of an automated system to classify retinal vessels into arteries and veins,” Comput. Methods Programs Biomed. 108(1), 367–376 (2012).
[Crossref] [PubMed]

Seidelmann, S. B.

S. B. Seidelmann, B. Claggett, P. E. Bravo, A. Gupta, H. Farhad, B. E. Klein, R. Klein, M. Di Carli, and S. D. Solomon, “Retinal vessel calibers in predicting long-term cardiovascular outcomes: the atherosclerosis risk in communities study,” Circulation 134(18), 1328–1338 (2016).
[Crossref] [PubMed]

Seijo, M. P.

S. Vázquez, B. Cancela, N. Barreira, M. G. Penedo, M. Rodríguez-Blanco, M. P. Seijo, G. C. de Tuero, M. A. Barceló, and M. Saez, “Improving retinal artery and vein classification by means of a minimal path approach,” Mach. Vis. Appl. 24(5), 919–930 (2013).
[Crossref]

Shelhamer, E.

E. Shelhamer, J. Long, and T. Darrell, “Fully convolutional networks for semantic segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017).
[Crossref] [PubMed]

Sjølie, A. K.

J. Grauslund, L. Hodgson, R. Kawasaki, A. Green, A. K. Sjølie, and T. Y. Wong, “Retinal vessel calibre and micro- and macrovascular complications in type 1 diabetes,” Diabetologia 52(10), 2213–2217 (2009).
[Crossref] [PubMed]

Solomon, S. D.

S. B. Seidelmann, B. Claggett, P. E. Bravo, A. Gupta, H. Farhad, B. E. Klein, R. Klein, M. Di Carli, and S. D. Solomon, “Retinal vessel calibers in predicting long-term cardiovascular outcomes: the atherosclerosis risk in communities study,” Circulation 134(18), 1328–1338 (2016).
[Crossref] [PubMed]

Song, Q.

X. Xu, M. Niemeijer, Q. Song, M. Sonka, M. K. Garvin, J. M. Reinhardt, and M. D. Abràmoff, “Vessel boundary delineation on fundus images using graph-based approach,” IEEE Trans. Med. Imaging 30(6), 1184–1191 (2011).
[Crossref] [PubMed]

Sonka, M.

X. Xu, M. Niemeijer, Q. Song, M. Sonka, M. K. Garvin, J. M. Reinhardt, and M. D. Abràmoff, “Vessel boundary delineation on fundus images using graph-based approach,” IEEE Trans. Med. Imaging 30(6), 1184–1191 (2011).
[Crossref] [PubMed]

M. D. Abràmoff, M. K. Garvin, and M. Sonka, “Retinal imaging and image analysis,” IEEE Rev. Biomed. Eng. 3, 169–208 (2010).
[Crossref] [PubMed]

Staal, J.

J. Staal, M. D. Abràmoff, M. Niemeijer, M. A. Viergever, and B. van Ginneken, “Ridge-based vessel segmentation in color images of the retina,” IEEE Trans. Med. Imaging 23(4), 501–509 (2004).
[Crossref] [PubMed]

Sun, J.

S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017).
[Crossref] [PubMed]

K. He, X. Zhang, S. Ren, and J. Sun, “Spatial Pyramid pooling in deep convolutional networks for visual recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015).
[Crossref] [PubMed]

Sutskever, I.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in International Conference on Neural Information Processing Systems, 2012), 1097–1105.

Ter Haar Romeny, B. M.

J. Zhang, B. Dashtbozorg, E. Bekkers, J. P. W. Pluim, R. Duits, and B. M. Ter Haar Romeny, “Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores,” IEEE Trans. Med. Imaging 35(12), 2631–2644 (2016).
[Crossref] [PubMed]

Tomasi, C.

R. Estrada, M. J. Allingham, P. S. Mettu, S. W. Cousins, C. Tomasi, and S. Farsiu, “Retinal Artery-Vein Classification via Topology Estimation,” IEEE Trans. Med. Imaging 34(12), 2518–2534 (2015).
[Crossref] [PubMed]

van Ginneken, B.

M. Niemeijer, X. Xu, A. V. Dumitrescu, P. Gupta, B. van Ginneken, J. C. Folk, and M. D. Abramoff, “Automated measurement of the arteriolar-to-venular width ratio in digital color fundus photographs,” IEEE Trans. Med. Imaging 30(11), 1941–1950 (2011).
[Crossref] [PubMed]

J. Staal, M. D. Abràmoff, M. Niemeijer, M. A. Viergever, and B. van Ginneken, “Ridge-based vessel segmentation in color images of the retina,” IEEE Trans. Med. Imaging 23(4), 501–509 (2004).
[Crossref] [PubMed]

Vázquez, S.

S. Vázquez, B. Cancela, N. Barreira, M. G. Penedo, M. Rodríguez-Blanco, M. P. Seijo, G. C. de Tuero, M. A. Barceló, and M. Saez, “Improving retinal artery and vein classification by means of a minimal path approach,” Mach. Vis. Appl. 24(5), 919–930 (2013).
[Crossref]

Viergever, M. A.

J. Staal, M. D. Abràmoff, M. Niemeijer, M. A. Viergever, and B. van Ginneken, “Ridge-based vessel segmentation in color images of the retina,” IEEE Trans. Med. Imaging 23(4), 501–509 (2004).
[Crossref] [PubMed]

Wang, J. J.

D. A. De Silva, G. Liew, M.-C. Wong, H.-M. Chang, C. Chen, J. J. Wang, M. L. Baker, P. J. Hand, E. Rochtchina, E. Y. Liu, P. Mitchell, R. I. Lindley, and T. Y. Wong, “Retinal vascular caliber and extracranial carotid disease in patients with acute ischemic stroke,” The Multi-Centre Retinal Stroke (MCRS) study 40(12), 3695–3699 (2009).
[Crossref] [PubMed]

Wang, S.

S. Wang, Y. Yin, G. Cao, B. Wei, Y. Zheng, and G. Yang, “Hierarchical retinal blood vessel segmentation based on feature and ensemble learning,” Neurocomputing 149, 708–717 (2015).
[Crossref]

Wang, T.

Q. Li, B. Feng, L. Xie, P. Liang, H. Zhang, and T. Wang, “A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images,” IEEE Trans. Med. Imaging 35(1), 109–118 (2016).
[Crossref] [PubMed]

Wang, X.

X. Xu, W. Ding, X. Wang, R. Cao, M. Zhang, P. Lv, and F. Xu, “Smartphone-based accurate analysis of retinal vasculature towards point-of-care diagnostics,” Sci. Rep. 6(1), 34603 (2016).
[Crossref] [PubMed]

Wei, B.

S. Wang, Y. Yin, G. Cao, B. Wei, Y. Zheng, and G. Yang, “Hierarchical retinal blood vessel segmentation based on feature and ensemble learning,” Neurocomputing 149, 708–717 (2015).
[Crossref]

Wong, M.-C.

D. A. De Silva, G. Liew, M.-C. Wong, H.-M. Chang, C. Chen, J. J. Wang, M. L. Baker, P. J. Hand, E. Rochtchina, E. Y. Liu, P. Mitchell, R. I. Lindley, and T. Y. Wong, “Retinal vascular caliber and extracranial carotid disease in patients with acute ischemic stroke,” The Multi-Centre Retinal Stroke (MCRS) study 40(12), 3695–3699 (2009).
[Crossref] [PubMed]

Wong, T. Y.

J. Grauslund, L. Hodgson, R. Kawasaki, A. Green, A. K. Sjølie, and T. Y. Wong, “Retinal vessel calibre and micro- and macrovascular complications in type 1 diabetes,” Diabetologia 52(10), 2213–2217 (2009).
[Crossref] [PubMed]

D. A. De Silva, G. Liew, M.-C. Wong, H.-M. Chang, C. Chen, J. J. Wang, M. L. Baker, P. J. Hand, E. Rochtchina, E. Y. Liu, P. Mitchell, R. I. Lindley, and T. Y. Wong, “Retinal vascular caliber and extracranial carotid disease in patients with acute ischemic stroke,” The Multi-Centre Retinal Stroke (MCRS) study 40(12), 3695–3699 (2009).
[Crossref] [PubMed]

Xie, L.

Q. Li, B. Feng, L. Xie, P. Liang, H. Zhang, and T. Wang, “A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images,” IEEE Trans. Med. Imaging 35(1), 109–118 (2016).
[Crossref] [PubMed]

Xu, F.

X. Xu, W. Ding, X. Wang, R. Cao, M. Zhang, P. Lv, and F. Xu, “Smartphone-based accurate analysis of retinal vasculature towards point-of-care diagnostics,” Sci. Rep. 6(1), 34603 (2016).
[Crossref] [PubMed]

Xu, X.

X. Xu, W. Ding, M. D. Abràmoff, and R. Cao, “An improved arteriovenous classification method for the early diagnostics of various diseases in retinal image,” Comput. Methods Programs Biomed. 141, 3–9 (2017).
[Crossref] [PubMed]

X. Xu, W. Ding, X. Wang, R. Cao, M. Zhang, P. Lv, and F. Xu, “Smartphone-based accurate analysis of retinal vasculature towards point-of-care diagnostics,” Sci. Rep. 6(1), 34603 (2016).
[Crossref] [PubMed]

X. Xu, M. Niemeijer, Q. Song, M. Sonka, M. K. Garvin, J. M. Reinhardt, and M. D. Abràmoff, “Vessel boundary delineation on fundus images using graph-based approach,” IEEE Trans. Med. Imaging 30(6), 1184–1191 (2011).
[Crossref] [PubMed]

M. Niemeijer, X. Xu, A. V. Dumitrescu, P. Gupta, B. van Ginneken, J. C. Folk, and M. D. Abramoff, “Automated measurement of the arteriolar-to-venular width ratio in digital color fundus photographs,” IEEE Trans. Med. Imaging 30(11), 1941–1950 (2011).
[Crossref] [PubMed]

Yang, G.

S. Wang, Y. Yin, G. Cao, B. Wei, Y. Zheng, and G. Yang, “Hierarchical retinal blood vessel segmentation based on feature and ensemble learning,” Neurocomputing 149, 708–717 (2015).
[Crossref]

Yin, Y.

S. Wang, Y. Yin, G. Cao, B. Wei, Y. Zheng, and G. Yang, “Hierarchical retinal blood vessel segmentation based on feature and ensemble learning,” Neurocomputing 149, 708–717 (2015).
[Crossref]

Zhang, H.

Q. Li, B. Feng, L. Xie, P. Liang, H. Zhang, and T. Wang, “A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images,” IEEE Trans. Med. Imaging 35(1), 109–118 (2016).
[Crossref] [PubMed]

Zhang, J.

J. Zhang, B. Dashtbozorg, E. Bekkers, J. P. W. Pluim, R. Duits, and B. M. Ter Haar Romeny, “Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores,” IEEE Trans. Med. Imaging 35(12), 2631–2644 (2016).
[Crossref] [PubMed]

Zhang, M.

X. Xu, W. Ding, X. Wang, R. Cao, M. Zhang, P. Lv, and F. Xu, “Smartphone-based accurate analysis of retinal vasculature towards point-of-care diagnostics,” Sci. Rep. 6(1), 34603 (2016).
[Crossref] [PubMed]

Zhang, X.

K. He, X. Zhang, S. Ren, and J. Sun, “Spatial Pyramid pooling in deep convolutional networks for visual recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015).
[Crossref] [PubMed]

Zheng, Y.

S. Wang, Y. Yin, G. Cao, B. Wei, Y. Zheng, and G. Yang, “Hierarchical retinal blood vessel segmentation based on feature and ensemble learning,” Neurocomputing 149, 708–717 (2015).
[Crossref]

Circulation (1)

S. B. Seidelmann, B. Claggett, P. E. Bravo, A. Gupta, H. Farhad, B. E. Klein, R. Klein, M. Di Carli, and S. D. Solomon, “Retinal vessel calibers in predicting long-term cardiovascular outcomes: the atherosclerosis risk in communities study,” Circulation 134(18), 1328–1338 (2016).
[Crossref] [PubMed]

Comput. Methods Programs Biomed. (2)

M. Saez, S. González-Vázquez, M. González-Penedo, M. A. Barceló, M. Pena-Seijo, G. Coll de Tuero, and A. Pose-Reino, “Development of an automated system to classify retinal vessels into arteries and veins,” Comput. Methods Programs Biomed. 108(1), 367–376 (2012).
[Crossref] [PubMed]

X. Xu, W. Ding, M. D. Abràmoff, and R. Cao, “An improved arteriovenous classification method for the early diagnostics of various diseases in retinal image,” Comput. Methods Programs Biomed. 141, 3–9 (2017).
[Crossref] [PubMed]

Diabetologia (1)

J. Grauslund, L. Hodgson, R. Kawasaki, A. Green, A. K. Sjølie, and T. Y. Wong, “Retinal vessel calibre and micro- and macrovascular complications in type 1 diabetes,” Diabetologia 52(10), 2213–2217 (2009).
[Crossref] [PubMed]

IEEE Rev. Biomed. Eng. (1)

M. D. Abràmoff, M. K. Garvin, and M. Sonka, “Retinal imaging and image analysis,” IEEE Rev. Biomed. Eng. 3, 169–208 (2010).
[Crossref] [PubMed]

IEEE Trans. Biomed. Eng. (3)

H. Narasimha-Iyer, J. M. Beach, B. Khoobehi, and B. Roysam, “Automatic identification of retinal arteries and veins from dual-wavelength images using structural and functional features,” IEEE Trans. Biomed. Eng. 54(8), 1427–1435 (2007).
[Crossref] [PubMed]

M. S. Miri and A. Mahloojifar, “Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction,” IEEE Trans. Biomed. Eng. 58(5), 1183–1192 (2011).
[Crossref] [PubMed]

S. Roychowdhury, D. D. Koozekanani, and K. K. Parhi, “Iterative Vessel Segmentation of Fundus Images,” IEEE Trans. Biomed. Eng. 62(7), 1738–1749 (2015).
[Crossref] [PubMed]

IEEE Trans. Image Process. (1)

B. Dashtbozorg, A. M. Mendonça, and A. Campilho, “An Automatic Graph-Based Approach for Artery/Vein Classification in Retinal Images,” IEEE Trans. Image Process. 23(3), 1073–1083 (2014).
[Crossref] [PubMed]

IEEE Trans. Med. Imaging (8)

R. Estrada, M. J. Allingham, P. S. Mettu, S. W. Cousins, C. Tomasi, and S. Farsiu, “Retinal Artery-Vein Classification via Topology Estimation,” IEEE Trans. Med. Imaging 34(12), 2518–2534 (2015).
[Crossref] [PubMed]

Q. Li, B. Feng, L. Xie, P. Liang, H. Zhang, and T. Wang, “A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images,” IEEE Trans. Med. Imaging 35(1), 109–118 (2016).
[Crossref] [PubMed]

J. Staal, M. D. Abràmoff, M. Niemeijer, M. A. Viergever, and B. van Ginneken, “Ridge-based vessel segmentation in color images of the retina,” IEEE Trans. Med. Imaging 23(4), 501–509 (2004).
[Crossref] [PubMed]

E. Ricci and R. Perfetti, “Retinal blood vessel segmentation using line operators and support vector classification,” IEEE Trans. Med. Imaging 26(10), 1357–1365 (2007).
[Crossref] [PubMed]

D. Marin, A. Aquino, M. E. Gegundez-Arias, and J. M. Bravo, “A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features,” IEEE Trans. Med. Imaging 30(1), 146–158 (2011).
[Crossref] [PubMed]

J. Zhang, B. Dashtbozorg, E. Bekkers, J. P. W. Pluim, R. Duits, and B. M. Ter Haar Romeny, “Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores,” IEEE Trans. Med. Imaging 35(12), 2631–2644 (2016).
[Crossref] [PubMed]

X. Xu, M. Niemeijer, Q. Song, M. Sonka, M. K. Garvin, J. M. Reinhardt, and M. D. Abràmoff, “Vessel boundary delineation on fundus images using graph-based approach,” IEEE Trans. Med. Imaging 30(6), 1184–1191 (2011).
[Crossref] [PubMed]

M. Niemeijer, X. Xu, A. V. Dumitrescu, P. Gupta, B. van Ginneken, J. C. Folk, and M. D. Abramoff, “Automated measurement of the arteriolar-to-venular width ratio in digital color fundus photographs,” IEEE Trans. Med. Imaging 30(11), 1941–1950 (2011).
[Crossref] [PubMed]

IEEE Trans. Pattern Anal. Mach. Intell. (4)

S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017).
[Crossref] [PubMed]

K. He, X. Zhang, S. Ren, and J. Sun, “Spatial Pyramid pooling in deep convolutional networks for visual recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015).
[Crossref] [PubMed]

R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Region-Based Convolutional Networks for Accurate Object Detection and Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 38(1), 142–158 (2016).
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E. Shelhamer, J. Long, and T. Darrell, “Fully convolutional networks for semantic segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017).
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Image Vis. Comput. (1)

K. Rothaus, X. Jiang, and P. Rhiem, “Separation of the retinal vascular graph in arteries and veins based upon structural knowledge,” Image Vis. Comput. 27(7), 864–875 (2009).
[Crossref]

J. Med. Imaging (Bellingham) (1)

Q. Hu, M. D. Abràmoff, and M. K. Garvin, “Automated construction of arterial and venous trees in retinal images,” J. Med. Imaging (Bellingham) 2(4), 044001 (2015).
[Crossref] [PubMed]

Mach. Vis. Appl. (1)

S. Vázquez, B. Cancela, N. Barreira, M. G. Penedo, M. Rodríguez-Blanco, M. P. Seijo, G. C. de Tuero, M. A. Barceló, and M. Saez, “Improving retinal artery and vein classification by means of a minimal path approach,” Mach. Vis. Appl. 24(5), 919–930 (2013).
[Crossref]

Nature (1)

R. F. Gariano and T. W. Gardner, “Retinal angiogenesis in development and disease,” Nature 438(7070), 960–966 (2005).
[Crossref] [PubMed]

Neurocomputing (1)

S. Wang, Y. Yin, G. Cao, B. Wei, Y. Zheng, and G. Yang, “Hierarchical retinal blood vessel segmentation based on feature and ensemble learning,” Neurocomputing 149, 708–717 (2015).
[Crossref]

Sci. Rep. (1)

X. Xu, W. Ding, X. Wang, R. Cao, M. Zhang, P. Lv, and F. Xu, “Smartphone-based accurate analysis of retinal vasculature towards point-of-care diagnostics,” Sci. Rep. 6(1), 34603 (2016).
[Crossref] [PubMed]

The Multi-Centre Retinal Stroke (MCRS) study (1)

D. A. De Silva, G. Liew, M.-C. Wong, H.-M. Chang, C. Chen, J. J. Wang, M. L. Baker, P. J. Hand, E. Rochtchina, E. Y. Liu, P. Mitchell, R. I. Lindley, and T. Y. Wong, “Retinal vascular caliber and extracranial carotid disease in patients with acute ischemic stroke,” The Multi-Centre Retinal Stroke (MCRS) study 40(12), 3695–3699 (2009).
[Crossref] [PubMed]

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O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015), 234–241.
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M. Niemeijer, B. V. Ginneken, and M. Loog, “Comparative study of retinal vessel segmentation methods on a new publicly available database,” Proc. SPIE 5370, 648–656 (2004).
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A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in International Conference on Neural Information Processing Systems, 2012), 1097–1105.

G. Mirsharif, F. Tajeripour, F. Sobhanmanesh, H. Pourreza, and T. Banaee, “Developing an automatic method for separation of arteries from veins in retinal images,” in 1st International Conference on Computer and Knowledge Engineering (2011).

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Figures (11)

Fig. 1
Fig. 1 Typical images and the corresponding manual labels from the DRIVE, INSPIRE, and REVEAL database. (a) and (f) are typical image and manual segmentation from the DRIVE data set. (b) and (g) are typical image and manual segmentation from the INSPIRE data set. It should be noted that the original manual segmentation only contains vessel centerlines of 1-pixel width, which is dilated for the sake of better visualization. (c)-(e) and (h)-(j) are typical images and manual segmentations from the REVEAL database. From left to right are images from REVEAL 1, 2, and 3, respectively.
Fig. 2
Fig. 2 Flowchart of the proposed method.
Fig. 3
Fig. 3 Histogram matching. The first row is the original color fundus images. The second row is the images after histogram matching. All images are matched to the top left image from the DRIVE data set.
Fig. 4
Fig. 4 Visualization of typical results on DRIVE data set. (a) and (d) are the original images. (b) and (e) are the ground truth labels. (c) and (f) are the results of automatic segmentation.
Fig. 5
Fig. 5 Illustration of the method assessment situation.
Fig. 6
Fig. 6 Visualization typical result from the INSPIRE data set. (a) and (d) are original color fundus images. (b) and (e) are the ground truth labels. It should be noted that the original ground truth label only contains vessel centerline pixels (single pixel width). (b) and (e) are dilated to a width of 6 pixels only for the sake of better visualization. (c) and (f) are the automatic segmentation results.
Fig. 7
Fig. 7 Visualization of the simultaneous arteriole and venule segmentation on REVEAL1 data set. REVEAL1 contains ten images with early signs of diabetic retinopathy. (a) and (e) are the original color fundus images. (b) and (f) are the manual segmentation from G1. (c) and (g) are the manual segmentation from G2. (d) and (h) are the results of automatic segmentation using proposed method.
Fig. 8
Fig. 8 Visualization of automatic arteriole and venule segmentation on REVEAL2, which includes severe signs of diabetic retinopathy. (a) and (d) are the original color fundus images. (b) and (e) are the manual segmentation results. (c) and (f) are the results of automatic segmentation using proposed method.
Fig. 9
Fig. 9 Visualization of automatic arteriole and venule segmentation on REVEAL3, which contains ten color fundus images acquired using a smartphone. (a) and (d) are the original color fundus images. (b) and (e) are the manual segmentation results. (c) and (f) are the results of automatic segmentation using proposed method.
Fig. 10
Fig. 10 The ROC of different methods and manual classification on REVEAL data sets. (a) ROC for arterioles segmentations. The AUC is 0.980 for DRIVE and 0.979, 0.969, and 0.926 for the three REVEAL data sets. (b) ROC for venule segmentations. The AUC is 0.981 for DRIVE and 0.987, 0.975, 0.967 for the three REVEAL data sets.
Fig. 11
Fig. 11 Sample color-coded result images for the DRIVE and REVEAL1 data sets. The corresponding colors are defined in Table 4.

Tables (4)

Tables Icon

Table 1 Comparison of different vessel segmentation algorithms on DRIVE

Tables Icon

Table 2 Comparison of different arteriole and venule classification algorithms on public data sets

Tables Icon

Table 3 Result on the REVEAL data sets

Tables Icon

Table 4 The true background, vein, artery and predicted background, vein, artery for DRIVE and RVEAL 1, 2, 3.

Equations (5)

Equations on this page are rendered with MathJax. Learn more.

E= xΩ w c (x)log( p l(x) (x))
Se= TP TP+FN ,Sp= TN TN+FP ,
Acc= TP+TN TP+FP+TN+FN
MIS C v = FP a TP v +FP a ,MIS C a = FP v TP a +FP v ,
Acc'=1 MIS C a +MIS C v 2

Metrics