Attention Based Fully Convolutional Neural Network for Simultaneous Detection and Segmentation of Optic Disc in Retinal Fundus Images

Accurate segmentation of the optic disc is very
important for computer-aided diagnosis of several ocular diseases
such as glaucoma, diabetic retinopathy, and hypertensive retinopathy.
The paper presents an accurate and fast optic disc detection and
segmentation method using an attention based fully convolutional
network. The network is trained from scratch using the fundus images
of extended MESSIDOR database and the trained model is used for
segmentation of optic disc. The false positives are removed based on
morphological operation and shape features. The result is evaluated
using three-fold cross-validation on six public fundus image databases
such as DIARETDB0, DIARETDB1, DRIVE, AV-INSPIRE, CHASE
DB1 and MESSIDOR. The attention based fully convolutional
network is robust and effective for detection and segmentation of
optic disc in the images affected by diabetic retinopathy and it
outperforms existing techniques.




References:
[1] R. R. Bourne, S. R. Flaxman, T. Braithwaite, M. V. Cicinelli,
A. Das, J. B. Jonas, J. Keeffe, J. H. Kempen, J. Leasher,
H. Limburg et al., “Magnitude, temporal trends, and projections
of the global prevalence of blindness and distance and near
vision impairment: a systematic review and meta-analysis,” The
Lancet Global Health, vol. 5, no. 9, pp. e888–e897, 2017.
[2] D. Pascolini and S. P. Mariotti, “Global estimates of visual
impairment: 2010,” British Journal of Ophthalmology, vol. 96,
no. 5, pp. 614–618, 2012.
[3] J. Lowell, A. Hunter, D. Steel, A. Basu, R. Ryder, E. Fletcher,
and L. Kennedy, “Optic nerve head segmentation,” IEEE
Transactions on medical Imaging, vol. 23, no. 2, pp. 256–264,
2004.
[4] G. D. Joshi, J. Sivaswamy, and S. Krishnadas, “Optic disk
and cup segmentation from monocular color retinal images for
glaucoma assessment,” IEEE transactions on medical imaging,
vol. 30, no. 6, pp. 1192–1205, 2011.
[5] J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N.-M. Tan,
D. Tao, C.-Y. Cheng, T. Aung, and T. Y. Wong, “Superpixel
classification based optic disc and optic cup segmentation for
glaucoma screening,” IEEE Transactions on Medical Imaging,
vol. 32, no. 6, pp. 1019–1032, 2013.
[6] R. J. Qureshi, L. Kovacs, B. Harangi, B. Nagy, T. Peto, and
A. Hajdu, “Combining algorithms for automatic detection of
optic disc and macula in fundus images,” Computer Vision and
Image Understanding, vol. 116, no. 1, pp. 138–145, 2012.
[7] R. Estrada, C. Tomasi, S. C. Schmidler, and S. Farsiu, “Tree
topology estimation,” IEEE transactions on pattern analysis and
machine intelligence, vol. 37, no. 8, pp. 1688–1701, 2015.
[8] A. Aquino, M. E. Geg´undez-Arias, and D. Mar´ın, “Detecting
the optic disc boundary in digital fundus images using
morphological, edge detection, and feature extraction
techniques,” IEEE transactions on medical imaging, vol. 29,
no. 11, pp. 1860–1869, 2010.
[9] S. Barrett, E. Naess, and T. Molvik, “Employing the hough
transform to locate the optic disk.” Biomedical sciences
instrumentation, vol. 37, pp. 81–86, 2001.
[10] C. Sinthanayothin, J. F. Boyce, H. L. Cook, and T. H.
Williamson, “Automated localisation of the optic disc, fovea,
and retinal blood vessels from digital colour fundus images,”
British Journal of Ophthalmology, vol. 83, no. 8, pp. 902–910,
1999. [11] M. Blanco, M. G. Penedo, N. Barreira, M. Penas, and M. J.
Carreira, “Localization and extraction of the optic disc using the
fuzzy circular hough transform,” in International Conference on
Artificial Intelligence and Soft Computing. Springer, 2006, pp.
712–721.
[12] A. Hoover and M. Goldbaum, “Locating the optic nerve in a
retinal image using the fuzzy convergence of the blood vessels,”
IEEE transactions on medical imaging, vol. 22, no. 8, pp.
951–958, 2003.
[13] M. Foracchia, E. Grisan, and A. Ruggeri, “Detection of optic
disc in retinal images by means of a geometrical model of
vessel structure,” IEEE transactions on medical imaging, vol. 23,
no. 10, pp. 1189–1195, 2004.
[14] A. A.-H. A.-R. Youssif, A. Z. Ghalwash, and A. A. S. A.-R.
Ghoneim, “Optic disc detection from normalized digital fundus
images by means of a vessels’ direction matched filter,” IEEE
Transactions on Medical imaging, vol. 27, no. 1, pp. 11–18,
2008.
[15] A. Osareh, M. Mirmehdi, B. Thomas, and R. Markham,
“Comparison of colour spaces for optic disc localisation in
retinal images,” in Pattern Recognition, 2002. Proceedings. 16th
International Conference on, vol. 1. IEEE, 2002, pp. 743–746.
[16] H. Li and O. Chutatape, “Automated feature extraction in color
retinal images by a model based approach,” IEEE Transactions
on biomedical engineering, vol. 51, no. 2, pp. 246–254, 2004.
[17] A. Giachetti, L. Ballerini, and E. Trucco, “Accurate and reliable
segmentation of the optic disc in digital fundus images,” Journal
of Medical Imaging, vol. 1, no. 2, pp. 024 001–024 001, 2014.
[18] I. Soares, M. Castelo-Branco, and A. M. Pinheiro, “Optic disc
localization in retinal images based on cumulative sum fields,”
IEEE journal of biomedical and health informatics, vol. 20,
no. 2, pp. 574–585, 2016.
[19] D. Zhang and Y. Zhao, “Novel accurate and fast optic
disc detection in retinal images with vessel distribution and
directional characteristics,” IEEE journal of biomedical and
health informatics, vol. 20, no. 1, pp. 333–342, 2016.
[20] S. Roychowdhury, D. D. Koozekanani, S. N. Kuchinka,
and K. K. Parhi, “Optic disc boundary and vessel origin
segmentation of fundus images,” IEEE journal of biomedical
and health informatics, vol. 20, no. 6, pp. 1562–1574, 2016.
[21] G. Papandreou, I. Kokkinos, and P.-A. Savalle, “Modeling local
and global deformations in deep learning: Epitomic convolution,
multiple instance learning, and sliding window detection,” in
Computer Vision and Pattern Recognition (CVPR), 2015 IEEE
Conference on. IEEE, 2015, pp. 390–399.
[22] X. Chen, R. Mottaghi, X. Liu, S. Fidler, R. Urtasun, and
A. Yuille, “Detect what you can: Detecting and representing
objects using holistic models and body parts,” in Proceedings
of the IEEE Conference on Computer Vision and Pattern
Recognition, 2014, pp. 1971–1978.
[23] M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler,
R. Benenson, U. Franke, S. Roth, and B. Schiele, “The
cityscapes dataset for semantic urban scene understanding,” in
Proceedings of the IEEE conference on computer vision and
pattern recognition, 2016, pp. 3213–3223.
[24] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and
A. L. Yuille, “Deeplab: Semantic image segmentation with deep
convolutional nets, atrous convolution, and fully connected crfs,”
arXiv preprint arXiv:1606.00915, 2016.
[25] O. Oktay, J. Schlemper, L. L. Folgoc, M. Lee, M. Heinrich,
K. Misawa, K. Mori, S. McDonagh, N. Y. Hammerla, B. Kainz
et al., “Attention u-net: Learning where to look for the pancreas,”
arXiv preprint arXiv:1804.03999, 2018.
[26] S. Jetley, N. A. Lord, N. Lee, and P. H. Torr, “Learn to pay
attention,” arXiv preprint arXiv:1804.02391, 2018.
[27] L. Fan, W.-C. Wang, F. Zha, and J. Yan, “Exploring new
backbone and attention module for semantic segmentation in
street scenes,” IEEE Access, vol. 6, pp. 71 566–71 580, 2018.
[28] S. Woo, J. Park, J.-Y. Lee, and I. So Kweon, “Cbam:
Convolutional block attention module,” in Proceedings of the
European Conference on Computer Vision (ECCV), 2018, pp.
3–19.
[29] D. Kingma and J. Ba, “Adam: A method for stochastic
optimization,” arXiv preprint arXiv:1412.6980, 2014.
[30] G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and
R. R. Salakhutdinov, “Improving neural networks by preventing
co-adaptation of feature detectors,” preprint arXiv:1207.0580,
2012.
[31] S. Morales, V. Naranjo, J. Angulo, and M. Alca˜niz, “Automatic
detection of optic disc based on pca and mathematical
morphology,” IEEE transactions on medical imaging, vol. 32,
no. 4, pp. 786–796, 2013.
[32] A. G. Salazar-Gonzalez, Y. Li, and X. Liu, “Optic disc
segmentation by incorporating blood vessel compensation,” in
Computational Intelligence In Medical Imaging (CIMI), 2011
IEEE Third International Workshop On. IEEE, 2011, pp. 1–8.
[33] D. Welfer, J. Scharcanski, C. M. Kitamura, M. M. Dal Pizzol,
L. W. Ludwig, and D. R. Marinho, “Segmentation of the optic
disk in color eye fundus images using an adaptive morphological
approach,” Computers in Biology and Medicine, vol. 40, no. 2,
pp. 124–137, 2010.
[34] D. Marin, M. E. Gegundez-Arias, A. Suero, and J. M. Bravo,
“Obtaining optic disc center and pixel region by automatic
thresholding methods on morphologically processed fundus
images,” Computer methods and programs in biomedicine, vol.
118, no. 2, pp. 173–185, 2015.
[35] H. Yu, E. S. Barriga, C. Agurto, S. Echegaray, M. S. Pattichis,
W. Bauman, and P. Soliz, “Fast localization and segmentation of
optic disk in retinal images using directional matched filtering
and level sets,” IEEE Transactions on information technology in
biomedicine, vol. 16, no. 4, pp. 644–657, 2012.