MultiScale and MultiBranch Convolutional Neural Network for Retinal Image Segmentation
Abstract
:1. Introduction
 We propose an effective multiscale and multibranch network (MSMBNet) model for the automatic segmentation of retinal vessels. The proposed network model is similarly used for the accurate joint segmentation of optic disc and optic cup;
 MSMBNet has the following advantages: (a) The multiscale context information fusion module uses skip connections and different expansion ratios of atrous convolution to improve the model’s full understanding of local context information. It improves the feature extraction ability of the network structure and maintains the correlation of features in the receptive field; (b) The multibranch convolution module combines convolutions of different receptive field sizes to improve the sensitivity to global context information; (c) Sideout rebuilding layer aggregates the effective features of different stages to improve the network learning ability without adding additional parameters and calculations;
 The network model proposed in this paper is tested on the DRIVE, STARE, CHASE_DB1 and DrishtiGS1 datasets. The proposed MSMBNet can obtain the most advanced results, which proves the robustness and effectiveness of the method.
2. Method
2.1. Network Structure
2.2. MultiScale Context Fusion Module
2.3. MultiBranch Convolution Module
2.4. SideOutput Rebuilding Layer
Algorithm 1: Sideoutput Rebuilding Layer 
Input: Feature map:$x\in {R}^{N\times h\times w\times c}$. Batch of feature maps: N. Height of the feature map:h. Width of the feature map:w. Channel c of the feature map:c. Downsampling factor:d. Output: Feature map after scale rebuilding: ${x}^{{}^{\prime}}\in {R}^{N\times (w\times d)\times (w\times d)\times \frac{c}{d\times d}}$

2.5. Attention Module
3. Dataset and Evaluation
3.1. Dataset
3.2. Implementation Details
3.3. Performance Evaluation
4. Experimental Results and Discussion
4.1. Compare the Results of the Improved Model
4.2. Retinal Vessel Segmentation
4.3. Optic Disc and Optic Cup Comparison of Different Methods
4.4. Different Segmentation Quantitative Analysis of the Results
4.5. Evaluation of ROC Curve and PR Curve
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
 Khan, K.B.; Khaliq, A.A.; Jalil, A.; Iftikhar, M.A.; Ullah, N.; Aziz, M.W.; Ullah, K.; Shahid, M. A review of retinal blood vessels extraction techniques: challenges, taxonomy, and future trends. Pattern Anal. Appl. 2019, 22, 767–802. [Google Scholar] [CrossRef]
 Franklin, S.W.; Rajan, S.E. Computerized screening of diabetic retinopathy employing blood vessel segmentation in retinal images. Biocybern. Biomed. Eng. 2014, 34, 117–124. [Google Scholar] [CrossRef]
 Jonas, J.B.; Bergua, A.; SchmitzValckenberg, P.; Papastathopoulos, K.I.; Budde, W.M. Ranking of optic disc variables for detection of glaucomatous optic nerve damage. Investig. Ophthalmol. Vis. Sci. 2000, 41, 1764. [Google Scholar]
 Miao, Y.; Cheng, Y. Automatic extraction of retinal blood vessel based on matched filtering and local entropy thresholding. In Proceedings of the 8th International Conference on Biomedical Engineering and Informatics (BMEI), Shenyang, China, 14–16 October 2015; pp. 62–67. [Google Scholar]
 Kundu, A.; Chatterjee, R.K. Retinal vessel segmentation using Morphological Angular ScaleSpace. In Proceedings of the 2012 Third International Conference on Emerging Applications of Information Technology, Kolkata, India, 30 November–1 December 2012; pp. 316–319. [Google Scholar] [CrossRef]
 PalomeraPerez, M.A.; MartinezPerez, M.E.; BenitezPerez, H. Parallel Multiscale Feature Extraction and Region Growing: Application in Retinal Blood Vessel Detection. IEEE Trans. Inf. Technol. Biomed. 2010, 14, 500–506. [Google Scholar] [CrossRef] [PubMed]
 Azzopardi, G.; Strisciuglio, N.; Vento, M.; Petkov, N. Trainable COSFIRE filters for vessel delineation with application to retinal images. Med. Image Anal. 2015, 19, 46–57. [Google Scholar] [CrossRef] [PubMed] [Green Version]
 Kumar, K.; Samal, D. Automated retinal vessel segmentation based on morphological preprocessing and 2DGabor wavelets. In Advanced Computing and Intelligent Engineering; Springer: Singapore, 2020; pp. 411–423. [Google Scholar]
 Tian, C.; Fang, T.; Fan, Y.; Wu, W. Multipath convolutional neural network in fundus segmentation of blood vessels. Biocybern. Biomed. Eng. 2020, 40, 583–595. [Google Scholar] [CrossRef]
 Jainish, G.R.; Jiji, G.W.; Infant, P.A. A novel automatic retinal vessel extraction using maximum entropy based EM algorithm. Multimed. Tools Appl. 2020, 79, 22337–22353. [Google Scholar] [CrossRef]
 Marín, D.; Aquino, A.; GegúndezArias, M.E.; Bravo, J.M. A new supervised method for blood vessel segmentation in retinal images by using graylevel and moment invariantsbased features. IEEE Trans. Med. Imaging 2010, 30, 146–158. [Google Scholar] [CrossRef] [PubMed] [Green Version]
 Aslani, S.; Sarnel, H. A new supervised retinal vessel segmentation method based on robust hybrid features. Biomed. Signal Process. Control 2016, 30, 1–12. [Google Scholar] [CrossRef]
 Feng, Z.; Yang, J.; Yao, L. Patchbased fully convolutional neural network with skip connections for retinal blood vessel segmentation. In Proceedings of the IEEE International Conference on Image Processing (ICIP), Beijing, China, 17–20 September 2017; pp. 1742–1746. [Google Scholar]
 Mo, J.; Zhang, L. Multilevel deep supervised networks for retinal vessel segmentation. Int. J. Comput. Assist. Radiol. Surg. 2017, 12, 2181–2193. [Google Scholar] [CrossRef]
 Hu, K.; Zhang, Z.; Niu, X.; Zhang, Y.; Cao, C.; Xiao, F.; Gao, X. Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved crossentropy loss function. Neurocomputing 2018, 39, 179–191. [Google Scholar] [CrossRef]
 Yan, Z.; Yang, X.; Cheng, K.T. A threestage deep learning model for accurate retinal vessel segmentation. IEEE J. Biomed. Health Inform. 2018, 23, 1427–1436. [Google Scholar] [CrossRef]
 Ronneberger, O.; Fischer, P.; Brox, T. Unet: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and ComputerAssisted Intervention; Springer: Cham, Switzerland, 2015; pp. 234–241. [Google Scholar]
 Alom, M.Z.; Hasan, M.; Yakopcic, C.; Taha, T.M.; Asari, V.K. Recurrent residual convolutional neural network based on unet (r2unet) for medical image segmentation. arXiv 2018, arXiv:1802.06955. [Google Scholar]
 Li, L.; Verma, M.; Nakashima, Y.; Nagahara, H.; Kawasaki, R. Iternet: Retinal image segmentation utilizing structural redundancy in vessel networks. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), Snowmass, CO, USA, 1–5 March 2020; pp. 3656–3665. [Google Scholar]
 Jin, Q.; Meng, Z.; Pham, T.D.; Chen, Q.; Wei, L.; Su, R. DUNet: A deformable network for retinal vessel segmentation. Knowl.Based Syst. 2019, 178, 149–162. [Google Scholar] [CrossRef] [Green Version]
 Atli, İ.; Gedik, O.S. SineNet: A fully convolutional deep learning architecture for retinal blood vessel segmentation. Eng. Sci. Technol. Int. J. 2020, in press. [Google Scholar]
 Wang, D.; Haytham, A.; Pottenburgh, J.; Saeedi, O.; Tao, Y. Hard Attention Net for Automatic Retinal Vessel Segmentation. IEEE J. Biomed. Health Inform. 2020, 24, 3384–3396. [Google Scholar] [CrossRef]
 Zilly, J.G.; Buhmann, J.M.; Mahapatra, D. Boosting convolutional filters with entropy sampling for optic cup and disc image segmentation from fundus images. In International Workshop on Machine Learning in Medical Imaging; Springer: Cham, Switzerland, 2015; pp. 136–143. [Google Scholar]
 Sevastopolsky, A.; Drapak, S.; Kiselev, K.; Snyder, B.M.; Keenan, J.D.; Georgievskaya, A. Stackunet: Refinement network for image segmentation on the example of optic disc and cup. arXiv 2018, arXiv:1804.11294. [Google Scholar]
 Chakravarty, A.; Sivaswamy, J. RACEnet: A recurrent neural network for biomedical image segmentation. IEEE J. Biomed. Health Inform. 2018, 23, 1151–1162. [Google Scholar] [CrossRef] [PubMed]
 Shah, S.; Kasukurthi, N.; Pande, H. Dynamic Region Proposal Networks For Semantic Segmentation In Automated Glaucoma Screening. In Proceedings of the IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy, 8–11 April 2019; pp. 578–582. [Google Scholar]
 Yu, S.; Xiao, D.; Frost, S.; Kanagasingam, Y. Robust optic disc and cup segmentation with deep learning for glaucoma detection. Comput. Med. Imaging Graph. 2019, 74, 61–71. [Google Scholar] [CrossRef] [PubMed]
 Ding, F.; Yang, G.; Liu, J.; Wu, J.; Ding, D.; Xv, J.; Cheng, G.; Li, X. Hierarchical Attention Networks for Medical Image Segmentation. arXiv 2019, arXiv:1911.08777. [Google Scholar]
 Gu, Z.; Cheng, J.; Fu, H.; Zhou, K.; Hao, H.; Zhao, Y.; Zhang, T.; Gao, S.; Liu, J. Cenet: Context encoder network for 2d medical image segmentation. IEEE Trans. Med. Imaging 2019, 38, 2281–2292. [Google Scholar] [CrossRef] [PubMed] [Green Version]
 Kadambi, S.; Wang, Z.; Xing, E. WGAN domain adaptation for the joint optic discandcup segmentation in fundus images. Int. J. Comput. Assist. Radiol. Surg. 2020. [Google Scholar] [CrossRef]
 Tabassum, M.; Khan, T.M.; Arslan, M.; Naqvi, S.S. CDEDNet: Joint Segmentation of Optic Disc and Optic Cup for Glaucoma Screening. IEEE Access 2020. [Google Scholar] [CrossRef]
 Chen, L.C.; Papandreou, G.; Schroff, F.; Adam, H. Rethinking atrous convolution for semantic image segmentation. arXiv 2017, arXiv:1706.05587. [Google Scholar]
 He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016. [Google Scholar]
 Chen, L.C.; Papandreou, G.; Kokkinos, I.; Murphy, K.; Yuille, A. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. Mach. Intell. 2017, 40, 834–848. [Google Scholar] [CrossRef]
 Zhao, H.; Shi, J.; Qi, X.; Wang, X.; Jia, J. Pyramid scene parsing network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21–26 July 2017; pp. 2881–2890. [Google Scholar]
 Woo, S.; Park, J.; Lee, J.Y.; Kweon, I.S. Cbam: Convolutional block attention module. In Proceedings of the European Conference on Computer Vision (ECCV); Springer: Cham, Switzerland, 2018. [Google Scholar] [CrossRef] [Green Version]
 Staal, J.; Abràmoff, M.D.; Niemeijer, M.; Viergever, M.A.; van Ginneken, B. Ridgebased vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 2004, 23, 501–509. [Google Scholar] [CrossRef] [PubMed]
 Hoover, A.D.; Kouznetsova, V.; Goldbaum, M. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med Imaging 2000, 19, 203–210. [Google Scholar] [CrossRef] [Green Version]
 Owen, C.G.; Rudnicka, A.R.; Mullen, R.; Barman, S.A.; Monekosso, D.; Whincup, P.H.; Ng, J.; Paterson, C. Measuring retinal vessel tortuosity in 10yearold children: Validation of the computerassisted image analysis of the retina (CAIAR) program. Investig. Ophthalmol. Vis. Sci. 2009, 50, 2004–2010. [Google Scholar] [CrossRef] [Green Version]
 Chakravarty, A.; Sivaswamy, J. Glaucoma classification with a fusion of segmentation and imagebased features. In Proceedings of the IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, Czech Republic, 13–16 April 2016; pp. 689–692. [Google Scholar]
 Paszke, A.; Gross, S.; Chintala, S.; Chanan, G.; Yang, E.; De Vito, Z.; Lin, Z.; Desmaison, A.; Antiga, L.; Lerer, A.; et al. Automatic Differentiation in Pytorch. In Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, 4–9 December 2017. [Google Scholar]
 Kingma, D.P.; Ba, J. Adam: A method for stochastic optimization. arXiv 2014, arXiv:1412.6980. [Google Scholar]
 Loshchilov, I.; Hutter, F. Decoupled weight decay regularization. arXiv 2017, arXiv:1711.05101. [Google Scholar]
 Zhuang, J. Laddernet: Multipath networks based on unet for medical image segmentation. arXiv 2018, arXiv:1810.07810. [Google Scholar]
 Soares, J.V.B.; Leandro, J.J.G.; Cesar, R.M.; Jelinek, H.F.; Cree, M.J. Retinal vessel segmentation using the 2D Gabor wavelet and supervised classification. IEEE Trans. Med. Imaging 2006, 25, 1214–1222. [Google Scholar] [CrossRef] [Green Version]
 Jiang, Y.; Zhang, H.; Tan, N.; Chen, L. Automatic retinal blood vessel segmentation based on fully convolutional neural networks. Symmetry 2019, 11, 1112. [Google Scholar] [CrossRef] [Green Version]
 Dharmawan, D.A.; Li, D.; Ng, B.P.; Rahardja, S. A new hybrid algorithm for retinal vessels segmentation on fundus images. IEEE Access 2019, 7, 41885–41896. [Google Scholar] [CrossRef]
 Fu, H.; Cheng, J.; Xu, Y.; Wong, D.W.K.; Liu, J.; Cao, X. Joint optic disc and cup segmentation based on multilabel deep network and polar transformation. IEEE Trans. Med. Imaging 2018, 37, 1597–1605. [Google Scholar] [CrossRef] [PubMed] [Green Version]
 Wang, L.; Yang, S.; Yang, S.; Zhao, C.; Tian, G.; Gao, Y.; Chen, Y.; Lu, Y. Automatic thyroid nodule recognition and diagnosis in ultrasound imaging with the YOLOv2 neural network. World J. Surg. Oncol. 2019, 17, 12. [Google Scholar] [CrossRef] [PubMed] [Green Version]
 Chakravarty, A.; Sivaswamy, J. Joint optic disc and cup boundary extraction from monocular fundus images. Comput. Methods Programs Biomed. 2017, 147, 51–61. [Google Scholar] [CrossRef]
 Joshi, G.D.; Sivaswamy, J.; Krishnadas, S.R. Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment. IEEE Trans. Med. Imaging 2011, 30, 1192–1205. [Google Scholar] [CrossRef]
 Joshi, G.D.; Sivaswamy, J.; Krishnadas, S.R. Depth discontinuitybased cup segmentation from multiview color retinal images. IEEE Trans. Biomed. Eng. 2012, 59, 1523–1531. [Google Scholar] [CrossRef]
 Cheng, J.; Liu, J.; Xu, Y.; Yin, F.; Wong, D.W.K.; Tan, N.M.; Tao, D.; Cheng, C.Y.; Aung, T.; Wong, T.Y. Superpixel classification based optic disc and optic cup segmentation for glaucoma screening. IEEE Trans. Med. Imaging 2013, 32, 1019–1032. [Google Scholar] [CrossRef]
 Zheng, Y.; Stambolian, D.; O’Brien, J.; Gee, C.J. Optic disc and cup segmentation from color fundus photograph using graph cut with priors. In International Conference on Medical Image Computing and ComputerAssisted Intervention; Springer: Berlin/Heidelberg, Germany, 2013; pp. 75–82. [Google Scholar]
Methods  DRIVE  STARE  CHASE  

F1  Acc  Se  Sp  F1  Acc  Se  Sp  F1  Acc  Se  Sp  
Basic  0.8263/0.0189  0.9707/0.0039  0.7957/0.0519  0.9864/0.0032  0.8307/0.0211  0.9742/0.0044  0.8470/0.0527  0.9848/0.0045  0.8081/ 0.0181  0.9754/0.0038  0.8220/0.0331  0.9857/0.0025 
SMCF  0.8280/0.0182  0.9705/ 0.0032  0.8093/0.0518  0.9860/0.0034  0.8317/0.0179  0.9743/0.0040  0.8476/0.0452  0.9848/0.0034  0.8140/ 0.0190  0.9762/0.0036  0.8256/0.0391  0.9863/0.0022 
SMCF+SRL  0.8292/0.0156  0.9702/0.0026  0.8240/0.0477  0.9843/0.0039  0.8336/0.0135  0.9747/0.0035  0.8512/0.0311  0.9849/0.0029  0.8150/0.0136  0.9763/ 0.0031  0.8273/0.0344  0.9863/0.0023 
SMCF+MBCM+SRL  0.8301/0.0143  0.9704/0.0028  0.8246/0.0487  0.9844/0.0038  0.8341/0.0119  0.9747/0.0032  0.8534/0.0299  0.9847/0.0032  0.8161/0.0168  0.9757/0.0045  $\mathbf{0}.\mathbf{8465}/\mathbf{0}.\mathbf{0290}$  0.9844/0.0020 
MSMBNet (ours)  $\mathbf{0}.\mathbf{8320}/\mathbf{0}.\mathbf{0136}$  $\mathbf{0}.\mathbf{9708}/\mathbf{0}.\mathbf{0026}$  $\mathbf{0}.\mathbf{8341}/\mathbf{0}.\mathbf{0471}$  $\mathbf{0}.\mathbf{9875}/\mathbf{0}.\mathbf{0032}$  $\mathbf{0}.\mathbf{8469}/\mathbf{0}.\mathbf{0110}$  $\mathbf{0}.\mathbf{9753}/\mathbf{0}.\mathbf{0034}$  $\mathbf{0}.\mathbf{8760}/\mathbf{0}.\mathbf{0211}$  $\mathbf{0}.\mathbf{9899}/\mathbf{0}.\mathbf{0020}$  $\mathbf{0}.\mathbf{8192}/\mathbf{0}.\mathbf{0165}$  $\mathbf{0}.\mathbf{9767}/\mathbf{0}.\mathbf{0034}$  0.8371/0.0280  $\mathbf{0}.\mathbf{9866}/\mathbf{0}.\mathbf{0020}$ 
Methods  Disc  Cup  Optic  

F1  Acc  Se  BLE  F1  Acc  Se  BLE  F1  Acc  Se  Sp  
Basic  0.9604/0.101  0.9935/0.003  0.8056/0.125  7.327/6.191  0.8834/0.112  0.9935/0.001  0.9417/0.084  17.528/11.964  0.9596/0.031  0.9968/0.002  0.9766/0.031  0.9974/0.002 
SMCF  0.8280/0.072  0.9949/0.002  0.8180/0.110  6.196/5.388  0.8959/0.096  0.9970/0.001  0.9498/0.063  16.289/10.575  0.9687/0.017  0.9979/0.001  0.9775/0.027  0.9985/0.001 
SMCF+SRL  0.9741/0.069  0.9952/0.002  0.8114/0.110  5.410/4.871  0.8999/0.109  0.9968/0.001  $\mathbf{0}.\mathbf{9599}/\mathbf{0}.\mathbf{056}$  15.086/11.286  0.9736/0.015  0.9983/0.0009  0.9831/0.021  0.9987/0.001 
SMCF+MBCM+SRL  0.9750/0.055  0.9953/0.002  0.8355/0.081  4.459/2.203  0.8995/0.106  0.9969/0.001  0.9558/0.063  13.354/10.111  0.9735/0.011  0.9983/0.0007  0.9833/0.016  0.9988/0.0009 
MSMBNet (ours)  $\mathbf{0}.\mathbf{9782}/\mathbf{0}.\mathbf{034}$  $\mathbf{0}.\mathbf{9959}/\mathbf{0}.\mathbf{001}$  $\mathbf{0}.\mathbf{8610}/\mathbf{0}.\mathbf{045}$  $\mathbf{3}.\mathbf{989}/\mathbf{1}.\mathbf{824}$  $\mathbf{0}.\mathbf{9184}/\mathbf{0}.\mathbf{091}$  $\mathbf{0}.\mathbf{9975}/\mathbf{0}.\mathbf{002}$  0.9560/0.050  $\mathbf{11}.\mathbf{017}/\mathbf{9}.\mathbf{240}$  $\mathbf{0}.\mathbf{9770}/\mathbf{0}.\mathbf{011}$  $\mathbf{0}.\mathbf{9985}/\mathbf{0}.\mathbf{0007}$  $\mathbf{0}.\mathbf{9862}/\mathbf{0}.\mathbf{018}$  $\mathbf{0}.\mathbf{9992}/\mathbf{0}.\mathbf{001}$ 
Type  Methods  Year  Se  Sp  Acc  F1 

Unsupervised methods  2nd human expert  0.7743  0.9819  0.9637  0.7889  
Miao et al. [4]  2015  0.7481  0.9748  0.9597    
Kumar et al. [8]  2019  0.7503  0.9717  0.9432    
Tian et al. [9]  2019  0.8639  0.9690  0.9580    
Jainish et al. [10]  2020      0.9657    
Supervised methods  Marín et al. [11]  2010  0.7607  0.9801  0.9452   
Aslani et al. [12]  2016  0.7545  0.9801  0.9513    
Feng et al. [13]  2017  0.7811  0.9839  0.9560    
UNet [17]  2018  0.7537  0.9820  0.9531  0.8142  
R2UNet [18]  2018  0.7792  0.9813  0.9556  0.8171  
IterNet [19]  2019  0.7735  0.9838  0.9573  0.8205  
Cenet [29]  2019  0.8309    0.9545    
SineNet [21]  2020  0.8260  0.9824  0.9685    
HAnet [22]  2020  0.7991  0.9813  0.9581  0.8293  
MSMBNet (ours)  2020  0.8283  0.9864  0.9708  0.8315 
Type  Methods  Year  Se  Sp  Acc  F1 

Unsupervised methods  2nd human expert  0.9017  0.9564  0.9522  0.7417  
Miao et al. [4]  2015  0.7298  0.9831  0.9532    
Azzopardi et al. [7]  2015  0.7716  0.9701  0.9497    
Jainish et al. [10]  2020      0.9703    
Supervised methods  Marín et al. [11]  2010  0.6944  0.9819  0.9526   
Aslani et al. [12]  2016  0.7556  0.9837  0.9605    
Mo et al. [14]  2017  0.8147  0.9844  0.9674    
Hu et al. [15]  2018  0.7543  0.9814  0.9632    
UNet [17]  2018  0.8270  0.9842  0.9690  0.8373  
IterNet [19]  2019  0.7715  0.9886  0.9701  0.8146  
DUNet [20]  2019  0.8369  0.9888  0.9773  0.8485  
SineNet [21]  2020  0.6776  0.9946  0.9711    
HAnet [22]  2020  0.8186  0.9844  0.9673  0.8379  
MSMBNet (ours)  2020  0.8760  0.9899  0.9753  0.8469 
Type  Methods  Year  Se  Sp  Acc  F1 

Unsupervised methods  2nd human expert  0.6776  0.9946  0.9711    
Azzopardi et al. [7]  2015  0.7585  0.9587  0.9387    
Tian et al. [9]  2019  0.8778  0.9680  0.9601    
Supervised methods  Mo et al. [14]  2017  0.7661  0.9816  0.9599   
Yan et al. [16]  2018  0.7641  0.9806  0.9607    
UNet [17]  2018  0.8288  0.9701  0.9578  0.7783  
R2UNet [18]  2018  0.7756  0.9820  0.9634  0.7928  
IterNet [19]  2019  0.7970  0.9823  0.9655  0.8073  
DUNet [20]  2019  0.8155  0.9752  0.9610  0.7883  
SineNet [21]  2020  0.7856  0.9845  0.9676    
MSMBNet (ours)  2020  0.8331  0.9864  0.9767  0.8190 
Methods  Year  Se  Sp  Acc  Dice  BLE 

Vessel Bend [51]  2011        0.9600/0.02  8.93/2.96 
Multiview [52]  2012        0.9600/0.02  8.93/2.96 
Superpixel [53]  2013        0.9500/0.02  9.38/5.75 
Graph Cut [54]  2013        0.9400/0.06  14.74/15.66 
UNet [17]  2015  0.9600  0.9800  0.9700  0.9500   
Zilly et al. [23]  2015        0.9470   
BCRF [50]  2017        0.9700/0.02  6.61/3.55 
Stackunet [24]  2018        0.9700/0.02  6.47/3.51 
RACEnet [25]  2018        0.9700/0.02  6.06/3.84 
Shah et al. [26]  2019        0.9600   
Yu et al. [27]  2019        0.9738   
Ding et al. [28]  2019        0.9721   
Cenet [29]  2019  0.9759  0.9990    0.9642   
WGAN [30]  2020        0.9540   
CDEDNet [31]  2020  0.9754  0.9973    0.9597   
MSMBNet (ours)  2020  0.9610  0.9984  0.9959  0.9782  3.98/1.82 
Methods  Year  Se  Sp  Acc  Dice  BLE 

Vessel Bend [51]  2011        0.7700/0.20  30.51/24.80 
Multiview [52]  2012        0.7900/0.18  25.28/18.00 
Superpixel [53]  2013        0.8000/0.14  22.04/12.57 
Graph Cut [54]  2013        0.7700/0.16  26.70/16.67 
UNet [17]  2015  0.9600  0.9800  0.9700  0.8500/0.10  19.53/13.98 
Zilly et al. [23]  2015        0.8300   
BCRF [50]  2017        0.8300/0.15  18.61/13.02 
Stackunet [24]  2018        0.8900/0.09  14.39/7.18 
RACEnet [25]  2018        0.8700/0.09  16.13/7.63 
Shah et al. [26]  2019        0.8900   
Yu et al. [27]  2019        0.8877   
Ding et al. [28]  2019        0.8513   
Cenet [29]  2019  0.8819  0.9909    0.8818   
WGAN [30]  2020        0.8400   
CDEDNet [31]  2020  0.9567  0.9981    0.9240   
MSMBNet (ours)  2020  0.9560  0.9983  0.9975  0.9184  13.01/9.24 
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. 
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Jiang, Y.; Liu, W.; Wu, C.; Yao, H. MultiScale and MultiBranch Convolutional Neural Network for Retinal Image Segmentation. Symmetry 2021, 13, 365. https://doi.org/10.3390/sym13030365
Jiang Y, Liu W, Wu C, Yao H. MultiScale and MultiBranch Convolutional Neural Network for Retinal Image Segmentation. Symmetry. 2021; 13(3):365. https://doi.org/10.3390/sym13030365
Chicago/Turabian StyleJiang, Yun, Wenhuan Liu, Chao Wu, and Huixiao Yao. 2021. "MultiScale and MultiBranch Convolutional Neural Network for Retinal Image Segmentation" Symmetry 13, no. 3: 365. https://doi.org/10.3390/sym13030365