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
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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 
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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