Abstract
Segmentation of retinal vessels in fundus images plays a very important role in diagnosing relevant diseases. In this paper, we have constructed automated segmentation models for the retinal vessel segmentation task based on convolutional neural networks. Since some typical deep convolutional neural networks need to be fed by high-resolution patches, small retinal patches should be interpolated to the specific resolution. The interpolated patches sometimes would introduce additional noises. Thus, we modify some typical deep architectures by inserting a set of convolutional layers. In this way, our models have the ability to adapt to different resolutions. Overall, five models are analyzed and compared in our studies including LeNet, M-AlexNet (modified AlexNet), M-ZF-Net (Modified ZF-Net), M-VGG (Modified VGG) and Deformable-ConvNet. Deformable-ConvNet captures the vascular structure and is used to do the retinal vessel segmentation task for the first time. We train the models from scratch and compare their ability to discriminate vessels/non-vessel pixels on two retinal fundus image datasets, DRIVE and STARE. Results are analyzed and compared in our studies. We obtain the highest accuracy of 0.9628/0.9690, lowest loss of 0.1045/0.0968, and highest AUC of 0.9764/0.9844 on DRIVE/STARE respectively. We also compare the CNN models with other segmentation methods. The results demonstrate the high effectiveness of the CNN-based approaches.
Similar content being viewed by others
References
Li Q, Feng B, **e LP, Liang P, Zhang H, Wang T (2015) A cross-modality learning approach for vessel segmentation in retinal images. IEEE Trans Med Imaging 35(1):109–118
Smart TJ, Richards CJ, Bhatnagar R, Pavesio C, Agrawal R, Jones PH (2015) A study of red blood cell deformability in diabetic retinopathy using optical tweezers. In: Optical trap** and optical micromanipulation XII, vol 9548. International Society for Optics and Photonics, p 954825
Teng T, Lefley M, Claremont D (2002) Progress towards automated diabetic ocular screening: a review of image analysis and intelligent systems for diabetic retinopathy. Med Biol Eng Comput 40(1):2–13
Kirbas C, Quek FKH (2004) A review of vessel extraction techniques and algorithms. ACM Comput Surv 36(2):81–121
Fraz MM, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka AR, Owen CG, Barman SA (2012) Blood vessel segmentation methodologies in retinal images—A survey. Comput Methods Programs Biomed 108(1):407–433
Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: IEEE conference on computer vision and pattern recognition. pp 3431–3440
Zhang H, Niu Y, Chang S-F (2018) Grounding referring expressions in images by variational context. In: IEEE conference on computer vision and pattern recognition. pp 4158–4166
Zhang H, Kyaw Z, Chang S, Chua T-S (2017) Visual translation embedding network for visual relation detection. In: IEEE conference on computer vision and pattern recognition. pp 3107–3115
Liu A, Nie W, Gao Y, Yuting S (2018) View-based 3-D model retrieval: a benchmark. IEEE Trans Cybern 48(3):916–928
Nie W, Cheng H, Yuting S (2017) Modeling temporal information of mitotic for mitotic event detection. IEEE Trans Big Data 3(4):458–469
Dai J, Qi H, **ong Y, Li Y, Zhang G, Hu H, Wei Y (2017) Deformable convolutional networks. In: IEEE international conference on computer vision. pp 764–773
Zana F, Klein J (2001) Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans Image Process 10(7):1010–1019
Azzopardi G, Strisciuglio N, Vento M, Petkov N (2015) Trainable COSFIRE filters for vessel delineation with application to retinal images. Med Image Anal 19(1):46–57
Liu I, Sun Y (1993) Recursive tracking of vascular networks in angiograms based on the detection-deletion scheme. IEEE Trans Med Imaging 12(2):334–341
Tolias YA, Panas SM (1998) A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering. IEEE Trans Med Imaging 17(2):263–273
Lam BSY, Yan H (2008) A novel vessel segmentation algorithm for pathological retina images based on the divergence of vector fields. IEEE Trans Med Imaging 27(2):237–246
Chalakkal RJ, Abdulla W (2017) Automatic segmentation of retinal vasculature. In: IEEE international conference on acoustics, speech and signal processing. pp 886–890
Simo A, De Ves E (2001) Segmentation of macular fluorescein angiographies. A statistical approach. Pattern Recogn 34(4):795–809
Ng J, Clay ST, Barman SA, Fielder AR, Moseley MJ, Parker KH, Paterson C (2010) Maximum likelihood estimation of vessel parameters from scale space analysis. Image Vis Comput 28(1):55–63
Zhang L, Fisher M, Wang W (2015) Retinal vessel segmentation using multi-scale textons derived from keypoints. Comput Med Imaging Gr 45:47–56
Zardadi M, Mehrshad N, Razavi SM (2016) Unsupervised segmentation of retinal blood vessels using the human visual system line detection model. J Inform Syst Telecommun 4:125–133
Hamamoto Y, Uchimura S, Watanabe M, Yasuda T, Mitani Yoshihiro, Tomita Shingo (1998) A gabor filter-based method for recognizing handwritten numerals. Pattern Recogn 31(4):395–400
Nguyen V, Blumenstein M (2011) An application of the 2D Gaussian filter for enhancing feature extraction in off-line signature verification. In: International conference on document analysis and recognition. pp 339–343
Staal J, Abràmoff MD, Niemeijer M, Viergever MA, Van Ginneken B (2004) Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 23(4):501–509
Ricci E, Perfetti R (2007) Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans Med Imaging 26(10):1357–1365
Sinthanayothin C, Boyce JF, Cook HL, Williamson TH (1999) Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images. Br J Ophthalmol 83(8):902–910
Li X, Wang L, Sung E (2008) AdaBoost with SVM-based component classifiers. Eng Appl Artif Intell 21(5):785–795
Aslani S, Sarnel H (2016) A new supervised retinal vessel segmentation method based on robust hybrid features. Biomed Signal Process Control 30:1–12
Marín D, Aquino A, Gegúndez-Arias ME, Bravo JM (2011) 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
Cheng E, Liang D, Yi W, Zhu Y, Megalooikonomou V, Ling H (2014) Discriminative vessel segmentation in retinal images by fusing context-aware hybrid features. Mach Vis Appl 25(7):1779–1792
Zhang H, Kyaw Z, Yu J, Chang S (2017) PPR-FCN: weakly supervised visual relation detection via parallel pairwise R-FCN. In: International conference on computer vision, pp 4243–4251
Kim Y (2014) Convolutional neural networks for sentence classification. ar**v preprint ar**v:1408.5882
Graves A, Mohamed A-R, Hinton G (2013) Speech recognition with deep recurrent neural networks. In: IEEE international conference on ICASSP. IEEE, pp 6645–6649
He X, He Z, Song J, Liu Z, Jiang Y-G, Chua T-S (2018) NAIS: Neural attentive item similarity model for recommendation. IEEE Trans Knowl Data Eng 30(12):2354–2366
He X, He Z, Du X, Chua T-S (2018) Adversarial personalized ranking for recommendation. In: International ACM SIGIR conference on research and development in information retrieval. pp 355–364
He X, Liao L, Zhang H, Nie L, Hu X, Chua T-S (2017) Neural collaborative filtering. In: International conference on world wide web. pp 173–182
Chen J, Zhang H, He X, Nie L, Liu W, Chua T-S (2017) Attentive collaborative filtering: multimedia recommendation with feature- and item-level attention. In: International ACM SIGIR conference on research and development in information retrieval. pp 335–344
He X, Chua T-S (2017) Neural factorization machines for sparse predictive analytics. In: International ACM SIGIR conference on research and development in information retrieval. pp 355–364
Song HA, Lee SY (2013) Hierarchical representation using NMF. In: International conference on neural information processing. pp 466–473
Wang S, Yin Y, Cao G, Wei B, Zheng Yuanjie, Yang Gong** (2015) Hierarchical retinal blood vessel segmentation based on feature and ensemble learning. Neurocomputing 149:708–717
Fu H, Xu Y, Wong DWK, Liu J (2016) Retinal vessel segmentation via deep learning network and fully-connected conditional random fields. In: IEEE international symposium on biomedical imaging. pp 698–701
Lahiri A, Roy AG, Sheet D, Biswas PK (2016) Deep neural ensemble for retinal vessel segmentation in fundus images towards achieving label-free angiography. In: Engineering in medicine and biology society (EMBC). IEEE, pp 1340–1343
Son J, Park SJ, Jung K-H (2017) Retinal vessel segmentation in fundoscopic images with generative adversarial networks. ar**v preprint ar**v:1706.09318
Maninis K-K, Pont-Tuset J, Arbeláez P, Van Gool L (2016) Deep retinal image understanding. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 140–148
Chen Y (2017) A labeling-free approach to supervising deep neural networks for retinal blood vessel segmentation. ar**v preprint ar**v:1704.07502
Hoover AW, Kouznetsova VL, Goldbaum MH (2000) Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response 19:203–210
Arora R, Basu A, Mianjy P, Mukherjee A (2016) Understanding deep neural networks with rectified linear units. ar**v preprint ar**v:1611.01491
Lcun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: International conference on neural information processing systems. pp 1097–1105
Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: European conference on computer vision. Springer, pp 818–833
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. ar**v preprint ar**v:1409.1556
Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M et al. (2015) TensorFlow: large-scale machine learning on heterogeneous distributed systems. ar**v preprint ar**v:1603.04467
François C et al (2015) Keras. https://keras.io
Fraz M, Barman S, Remagnino P, Hoppe A, Basit Abdul W, Uyyanonvara Bunyarit, Rudnicka Alicja R, Owen Christopher G (2012) An approach to localize the retinal blood vessels using bit planes and centerline detection. Comput Methods Programs Biomed 108(2):600–616
Nguyen UTV, Bhuiyan A, Park LAF, Ramamohanarao K (2013) An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern Recogn 46(3):703–715
Fu H, Xu Y, Lin S, Wong DWK, Liu J (2016) DeepVessel: Retinal vessel segmentation via deep learning and conditional random field. In: International conference on medical image computing and computer-assisted intervention, pp 132–139
Melinščak M, Prentašić P, Lončarić S (2015) Retinal vessel segmentation using deep neural networks. In: International conference on computer vision theory and applications, pp 577–582
Roychowdhury S, Koozekanani DD, Parhi KK (2015) Iterative vessel segmentation of fundus images. IEEE Trans Biomed Eng 62(7):1738–1749
Acknowledgements
This work is supported by the Science and Technology Program of Tian**, China [Grant No. 16ZXHLGX00170], the National Key Technology R&D Program of China [Grant No. 2015BAH52F00] and the National Natural Science Foundation of China [Grant No. 61702361].
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
**, Q., Chen, Q., Meng, Z. et al. Construction of Retinal Vessel Segmentation Models Based on Convolutional Neural Network. Neural Process Lett 52, 1005–1022 (2020). https://doi.org/10.1007/s11063-019-10011-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11063-019-10011-1