# Automatic Retinal Blood Vessel Segmentation Based on Fully Convolutional Neural Networks

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## Abstract

**:**

## 1. Introduction

- (1)
- We delved into the effects of several data preprocessing methods on network performance. By performing grayscale, normalization, Contrast Limited Adaptive Histogram Equalization (CLAHE), and gamma correction on the retina image, the performance of the model can be improved.
- (2)
- We have devised a new data augmentation method for retinal images to enhance the performance of the model. It can be combined with existing data augmentation methods to achieve better results. We named it Random Crop and Fill (RCF).
- (3)
- We proposed M3FCN, an improved deep fully convolutional neural network structure, for retinal vessel automatic segmentation. Compared with the basic FCN, the M3FCN has the following three improvements: adding a multi-scale input module, expanding to a multi-path FCN, and obtaining the final segmentation result through multi-output fusion. The experimental results show that all three improvements can improve the performance of the model.
- (4)
- We obtain the final segmentation image by overlapping the sampling test patch and the overlapping patch reconstruction algorithm.
- (5)
- We have proved through the ablation analysis experiments that the various improvements proposed in this paper are effective. Experimental results show that the proposed framework is robust and that the improved method has the potential to extend to other methods and medical images.

## 2. Methodology

#### 2.1. Materials

#### 2.2. Dataset Preparation and Image Preprocessing

#### 2.2.1. Dataset Preparation

#### 2.2.2. Image Preprocessing

#### 2.3. Dynamic Patch Extraction

Algorithm 1 Training FCN with dynamic extraction patch strategy |

Input: Train images $X\in {\mathbb{R}}^{N\times 1\times H\times W}$, ground truths $G\in {\mathbb{R}}^{N\times 1\times H\times W}$.Input: Patch size p, dynamic patch number n.Input: Initial FCN parameter $\theta $, epochs E.Output: FCN parameter $\theta $.Initialize patch images $I\in {\mathbb{R}}^{n\times 1\times p\times p}$. Initialize patch labels $T\in {\mathbb{R}}^{n\times 1\times p\times p}$. for $e=1$ to E dofor $n=1$ to N dofor $k=1$ to $\lceil {\displaystyle \frac{n}{N}}\rceil $ doRandomly generate the center coordinates $(x,y)$ of the patch. Patches I and labels T are extracted from X and G centered on $(x,y)$, respectively. end forend for$loss={\displaystyle \frac{1}{n}}{\nabla}_{\theta}{\sum}_{i}^{n}L(f(I[i];\theta ),T[i])$. Update parameters $\theta $ using the Adam [28] optimizer. end forreturn $\theta $. |

Algorithm 2 Testing FCN with overlapping patches reconstruction algorithm |

Input: Test images $X\in {\mathbb{R}}^{N\times 1\times H\times W}$, patch size p, stride size s.Output: Final segmentation result ${O}^{\prime}$.${N}_{h}=\lceil (H-p)/S\rceil $, ${N}_{w}=\lceil (W-p)/S\rceil $. ${H}^{\prime}={N}_{h}\times p$, ${W}^{\prime}={N}_{w}\times p$. Zero padding for X to ${X}^{\prime}\in {\mathbb{R}}^{N\times 1\times {H}^{\prime}\times {W}^{\prime}}$. Initialize ${O}_{p}\in {\mathbb{R}}^{N\times 1\times {H}^{\prime}\times {W}^{\prime}}$. Initialize ${O}_{s}\in {\mathbb{R}}^{N\times 1\times {H}^{\prime}\times {W}^{\prime}}$. for $n=1$ to N dofor $h=1$ to ${N}_{h}$ dofor $w=1$ to ${N}_{w}$ doA patch $x\in {\mathbb{R}}^{1\times 1\times p\times p}$ is extracted with $(h\times s,w\times s)$ as the upper left coordinate. Input x into the trained FCN to get the output y. Assign y to the corresponding area of ${O}_{p}$. Assign 1 to the corresponding area of ${O}_{s}$. end forend forend for$O={O}_{p}/{O}_{s}$. Crop $O\in {\mathbb{R}}^{N\times 1\times {H}^{\prime}\times {W}^{\prime}}$ to get the final segmentation image ${O}^{\prime}\in {\mathbb{R}}^{N\times 1\times H\times W}$. return ${O}^{\prime}$ |

#### 2.4. A Novel Retinal Image Data Augmentation Method

#### 2.5. Fully Convolutional Neural Network (FCN)

#### 2.5.1. The Basic FCN Architecture

#### 2.5.2. Multi-Scale, Multi-Path, and Multi-Output Fusion FCN (M3FCN)

## 3. Experimental Setup

#### 3.1. Evaluation Metrics

#### 3.2. Implementation Details

## 4. Results and Discussion

#### 4.1. Ablation Analysis

#### 4.1.1. Validation of the Image Preprocessing

#### 4.1.2. The Impact of RCF’s Hyper-Parameters

#### 4.1.3. Validation of the Data Augmentation and RCF

#### 4.1.4. Comparisons with FCN and M3FCN

#### 4.2. Comparison with the Existing Methods

#### 4.3. Cross-Testing Evaluation

#### 4.4. Visualize the Results

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 3.**(

**a**) is the original image. (

**b**–

**d**) are visualizations of red, green, and blue channels, respectively. Results of each preprocessing strategy: (

**e**) grayscale; (

**f**) data normalization; (

**g**) CLAHE; (

**h**) Gamma correction.

**Figure 4.**Visualization of the Random Crop and Fill (RCF) method. (

**a**) cropping; (

**b**) RCF-R; (

**c**) RCF-0; (

**d**) RCF-A.

**Figure 9.**Visualization of the random samples results for each test dataset: (

**a**) DRIVE; (

**b**) STARE; (

**c**) CHASE.

**Figure 10.**Visualize the F1 score and comparison of the image segmentation results for each image in the test dataset: (

**a**) DRIVE; (

**b**) STARE; (

**c**) CHASE.

No. | Grayscale | Data Normalization | CLAHE | Gamma Correction | F1 | Accuracy | Sensitivity | Specificity | AUC |
---|---|---|---|---|---|---|---|---|---|

0 | 0.8048 | 0.9546 | 0.7350 | 0.9866 | 0.9771 | ||||

1 | ✓ | 0.8199 | 0.9692 | 0.8000 | 0.9855 | 0.9831 | |||

2 | ✓ | ✓ | 0.8229 | 0.9697 | 0.8043 | 0.9855 | 0.9852 | ||

3 | ✓ | ✓ | 0.8284 | 0.9702 | 0.8215 | 0.9845 | 0.9871 | ||

4 | ✓ | ✓ | 0.8168 | 0.9683 | 0.8081 | 0.9836 | 0.9839 | ||

5 | ✓ | ✓ | ✓ | 0.8299 | 0.9699 | 0.8376 | 0.9826 | 0.9873 | |

6 | ✓ | ✓ | ✓ | 0.8173 | 0.9678 | 0.8234 | 0.9816 | 0.9840 | |

7 | ✓ | ✓ | ✓ | 0.8292 | 0.9704 | 0.8206 | 0.9848 | 0.9873 | |

8 | ✓ | ✓ | ✓ | ✓ | 0.8321 | 0.9706 | 0.8325 | 0.9838 | 0.9880 |

Method | F1 | Accuracy | Sensitivity | Specificity | AUC |
---|---|---|---|---|---|

- | 0.8288 | 0.9703 | 0.8198 | 0.9848 | 0.9870 |

RCF-0 | 0.8299 | 0.9702 | 0.8298 | 0.9837 | 0.9873 |

RCF-R | 0.8294 | 0.9702 | 0.8269 | 0.9840 | 0.9873 |

RCF-A | 0.8321 | 0.9706 | 0.8325 | 0.9838 | 0.9880 |

**Table 3.**Test results with data augmentation and RCF-A on DRIVE based on M3FCN. DA: Data augmentation.

Method | F1 | Accuracy | Sensitivity | Specificity | AUC |
---|---|---|---|---|---|

0.8242 | 0.9697 | 0.8111 | 0.9849 | 0.9861 | |

DA | 0.8288 | 0.9703 | 0.8198 | 0.9848 | 0.9870 |

RCF-A | 0.8255 | 0.9689 | 0.8392 | 0.9814 | 0.9866 |

DA + RCF-A | 0.8321 | 0.9706 | 0.8325 | 0.9838 | 0.9880 |

Model Name | F1 | Accuracy | Sensitivity | Specificity | AUC |
---|---|---|---|---|---|

Basic FCN | 0.8286 | 0.9703 | 0.8196 | 0.9848 | 0.9870 |

Muiti-scale FCN | 0.8290 | 0.9707 | 0.8115 | 0.9860 | 0.9873 |

Multi-path FCN | 0.8287 | 0.9706 | 0.8118 | 0.9858 | 0.9871 |

Multi-output fusion FCN | 0.8293 | 0.9705 | 0.8192 | 0.9850 | 0.9870 |

Muiti-scale, multi-path FCN | 0.8295 | 0.9703 | 0.8259 | 0.9841 | 0.9870 |

Muiti-scale, multi-output fusion FCN | 0.8286 | 0.9708 | 0.8063 | 0.9866 | 0.9871 |

Muiti-path, multi-output fusion FCN | 0.8304 | 0.9701 | 0.8370 | 0.9828 | 0.9873 |

M3FCN | 0.8321 | 0.9706 | 0.8325 | 0.9838 | 0.9880 |

Methods | Year | F1 | Accuracy | Sensitivity | Specificity | AUC |
---|---|---|---|---|---|---|

2nd human expert | - | 0.7889 | 0.9637 | 0.7743 | 0.9819 | 0.8781 |

Lam et al. [35] | 2010 | - | 0.9472 | - | - | 0.9614 |

You et al. [11] | 2011 | - | 0.9434 | 0.7410 | 0.9751 | - |

Fraz et al. [36] | 2012 | - | 0.9430 | 0.7152 | 0.9759 | - |

Azzopardi et al. [37] | 2015 | - | 0.9442 | 0.7655 | 0.9704 | 0.9614 |

Ronneberger et al. [38] | 2015 | 0.8142 | 0.9531 | 0.7537 | 0.9820 | 0.9755 |

Liskowsk et al. [39] | 2016 | - | 0.9495 | 0.7763 | 0.9768 | 0.9720 |

Maninis et al. [40] | 2016 | 0.8210 | 0.9541 | 0.8261 | 0.9115 | 0.9861 |

Orlando et al. [41] | 2017 | 0.7857 | - | 0.7897 | 0.9684 | - |

Dasgupta et al. [42] | 2017 | 0.8074 | 0.9533 | 0.7691 | 0.9801 | 0.9744 |

Zhang et al. [43] | 2017 | 0.7953 | 0.9466 | 0.7861 | 0.9712 | 0.9703 |

Xia et al. [44] | 2018 | - | 0.9540 | 0.7740 | 0.9800 | - |

Alom et al. [45] | 2018 | 0.8171 | 0.9556 | 0.7792 | 0.9813 | 0.9784 |

Zhuang et al. [21] | 2018 | 0.8202 | 0.9561 | 0.7856 | 0.9810 | 0.9793 |

Lu et al. [46] | 2018 | - | 0.9634 | 0.7941 | 0.9870 | 0.9787 |

Oliveira et al. [27] | 2018 | - | 0.9576 | 0.8039 | 0.9804 | 0.9821 |

Jin et al. [19] | 2019 | 0.8237 | 0.9566 | 0.7963 | 0.9800 | 0.9802 |

Basic FCN (ours) | 2019 | 0.8286 | 0.9703 | 0.8197 | 0.9848 | 0.9874 |

M3FCN (ours) | 2019 | 0.8321 | 0.9706 | 0.8325 | 0.9838 | 0.9880 |

Methods | Year | F1 | Accuracy | Sensitivity | Specificity | AUC |
---|---|---|---|---|---|---|

2nd human expert | - | 0.7417 | 0.9522 | 0.9017 | 0.9564 | 0.9291 |

Lam et al. [35] | 2010 | - | 0.9567 | - | - | 0.9739 |

Fraz et al. [36] | 2012 | - | 0.9442 | 0.7311 | 0.9680 | - |

Azzopardi et al. [37] | 2015 | - | 0.9563 | 0.7716 | 0.9701 | 0.9497 |

Li et al. [47] | 2015 | - | 0.9628 | 0.7726 | 0.9844 | 0.9879 |

Ronneberger et al. [38] | 2015 | 0.8373 | 0.9690 | 0.8270 | 0.9842 | 0.9898 |

Liskowsk et al. [39] | 2016 | - | 0.9566 | 0.7867 | 0.9754 | 0.9785 |

Maninis et al. [40] | 2016 | 0.8210 | 0.9541 | 0.8261 | 0.9115 | 0.9861 |

Orlando et al. [41] | 2017 | 0.7701 | - | 0.7680 | 0.9738 | - |

Son et al. [48] | 2017 | 0.8353 | 0.9657 | 0.8350 | - | 0.9777 |

Zhang et al. [43] | 2017 | 0.7815 | 0.9547 | 0.7882 | 0.9729 | 0.9740 |

Oliveira et al. [27] | 2018 | - | 0.9694 | 0.8315 | 0.9858 | 0.9905 |

Xia et al. [44] | 2018 | - | 0.9530 | 0.7670 | 0.9770 | - |

Lu et al. [46] | 2018 | - | 0.9628 | 0.8090 | 0.9770 | 0.9801 |

Li et al. [49] | 2019 | 0.8435 | 0.9673 | 0.8465 | - | 0.9834 |

Jin et al. [19] | 2019 | 0.8143 | 0.9641 | 0.7595 | 0.9878 | 0.9832 |

Basic FCN (ours) | 2019 | 0.8485 | 0.9773 | 0.8369 | 0.9888 | 0.9917 |

M3FCN (ours) | 2019 | 0.8531 | 0.9777 | 0.8522 | 0.9880 | 0.9923 |

Methods | Year | F1 | Accuracy | Sensitivity | Specificity | AUC |
---|---|---|---|---|---|---|

2nd human expert | - | 0.7969 | 0.9733 | 0.8313 | 0.9829 | 0.9071 |

Lam et al. [35] | 2015 | - | 0.9387 | 0.7585 | 0.9587 | 0.9487 |

Li et al. [47] | 2015 | - | 0.9581 | 0.7507 | 0.9793 | 0.9793 |

Ronneberger et al. [38] | 2015 | 0.7783 | 0.9578 | 0.8288 | 0.9701 | 0.9772 |

Liskowsk et al. [39] | 2016 | - | 0.9566 | 0.7867 | 0.9754 | 0.9785 |

Zhang et al. [43] | 2017 | 0.7581 | 0.9502 | 0.7644 | 0.9716 | 0.9706 |

Zhang et al. [50] | 2018 | - | 0.9662 | 0.7742 | 0.9876 | 0.9865 |

Alom et al. [45] | 2018 | 0.7928 | 0.9634 | 0.7756 | 0.9820 | 0.9815 |

Zhuang et al. [21] | 2018 | 0.8031 | 0.9656 | 0.7978 | 0.9818 | 0.9839 |

Lu et al. [46] | 2018 | - | 0.9664 | 0.7571 | 0.9823 | 0.9752 |

Jin et al. [19] | 2019 | 0.7883 | 0.9610 | 0.8155 | 0.9752 | 0.9804 |

Basic FCN (ours) | 2019 | 0.8200 | 0.9770 | 0.8323 | 0.9867 | 0.9912 |

M3FCN (ours) | 2019 | 0.8243 | 0.9773 | 0.8453 | 0.9862 | 0.9917 |

**Table 8.**Comparison of experimental results: training models using the STARE dataset, then testing on the DRIVE dataset.

Method | Year | F1 | Accuracy | Sensitivity | Specificity | AUC |
---|---|---|---|---|---|---|

Fraz et al. [14] | 2012 | - | 0.9456 | 0.7242 | 0.9792 | 0.9697 |

Li et al. [47] | 2015 | - | 0.9486 | 0.7273 | 0.9810 | 0.9677 |

Yan et al. [51] | 2018 | - | 0.9444 | 0.7014 | 0.9802 | 0.9568 |

Jin et al. [19] | 2019 | - | 0.9481 | 0.6505 | 0.9914 | 0.9718 |

Basic FCN(ours) | 2019 | 0.7675 | 0.9646 | 0.6663 | 0.9933 | 0.9780 |

M3FCN (ours) | 2019 | 0.7845 | 0.9665 | 0.6950 | 0.9926 | 0.9820 |

**Table 9.**Comparison of experimental results: training models using the DRIVE dataset, then testing on the STARE dataset.

Method | Year | F1 | Accuracy | Sensitivity | Specificity | AUC |
---|---|---|---|---|---|---|

Fraz et al. [14] | 2012 | - | 0.9495 | 0.7010 | 0.9770 | 0.9660 |

Li et al. [47] | 2015 | - | 0.9545 | 0.7027 | 0.9828 | 0.9671 |

Yan et al. [51] | 2018 | - | 0.9580 | 0.7319 | 0.9840 | 0.9678 |

Jin et al. [19] | 2019 | - | 0.9445 | 0.8419 | 0.9563 | 0.9690 |

Basic FCN (ours) | 2019 | 0.7755 | 0.9633 | 0.8332 | 0.9740 | 0.9790 |

M3FCN (ours) | 2019 | 0.7876 | 0.9647 | 0.8604 | 0.9733 | 0.9826 |

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**MDPI and ACS Style**

Jiang, Y.; Zhang, H.; Tan, N.; Chen, L.
Automatic Retinal Blood Vessel Segmentation Based on Fully Convolutional Neural Networks. *Symmetry* **2019**, *11*, 1112.
https://doi.org/10.3390/sym11091112

**AMA Style**

Jiang Y, Zhang H, Tan N, Chen L.
Automatic Retinal Blood Vessel Segmentation Based on Fully Convolutional Neural Networks. *Symmetry*. 2019; 11(9):1112.
https://doi.org/10.3390/sym11091112

**Chicago/Turabian Style**

Jiang, Yun, Hai Zhang, Ning Tan, and Li Chen.
2019. "Automatic Retinal Blood Vessel Segmentation Based on Fully Convolutional Neural Networks" *Symmetry* 11, no. 9: 1112.
https://doi.org/10.3390/sym11091112