Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (10): 3275-3281.DOI: 10.11772/j.issn.1001-9081.2022091437
Special Issue: 多媒体计算与计算机仿真
• Multimedia computing and computer simulation • Previous Articles Next Articles
Zhiang ZHANG1(), Guangzhong LIAO2
Received:
2022-09-28
Revised:
2022-11-28
Accepted:
2022-12-13
Online:
2023-04-11
Published:
2023-10-10
Contact:
Zhiang ZHANG
About author:
LIAO Guangzhong, born in 1969, M. S., associate professor. His research interests include internet of things, information safety.
Supported by:
通讯作者:
张志昂
作者简介:
廖光忠(1969—),男,贵州贵阳人,副教授,硕士,CCF会员,主要研究方向:物联网、信息安全。
基金资助:
CLC Number:
Zhiang ZHANG, Guangzhong LIAO. Multi-scale feature enhanced retinal vessel segmentation algorithm based on U-Net[J]. Journal of Computer Applications, 2023, 43(10): 3275-3281.
张志昂, 廖光忠. 基于U-Net的多尺度特征增强视网膜血管分割算法[J]. 《计算机应用》唯一官方网站, 2023, 43(10): 3275-3281.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022091437
数据集 | 算法 | Acc | Pre | Se | F1 | AUC |
---|---|---|---|---|---|---|
DRIVE | U-Net | 0.932 3 | 0.924 2 | 0.793 1 | 0.853 6 | 0.943 6 |
ResU-Net | 0.934 1 | 0.934 8 | 0.832 3 | 0.880 5 | 0.954 3 | |
AttU-Net | 0.953 1 | 0.983 9 | 0.829 9 | 0.900 3 | 0.968 9 | |
CS-Net | 0.958 7 | 0.984 9 | 0.834 8 | 0.903 6 | 0.975 7 | |
MFEU-Net | 0.960 1 | 0.987 6 | 0.838 7 | 0.907 1 | 0.979 1 | |
CHASE_DB1 | U-Net | 0.934 5 | 0.960 4 | 0.802 1 | 0.874 1 | 0.923 6 |
ResU-Net | 0.942 1 | 0.964 5 | 0.820 4 | 0.886 6 | 0.938 5 | |
AttU-Net | 0.951 9 | 0.969 9 | 0.817 8 | 0.887 3 | 0.947 8 | |
CS-Net | 0.961 3 | 0.970 8 | 0.819 9 | 0.888 9 | 0.956 3 | |
MFEU-Net | 0.970 2 | 0.989 9 | 0.832 6 | 0.904 4 | 0.971 3 |
Tab. 1 Performance evaluation results of different algorithms on DRIVE and CHASE_DB1 datasets
数据集 | 算法 | Acc | Pre | Se | F1 | AUC |
---|---|---|---|---|---|---|
DRIVE | U-Net | 0.932 3 | 0.924 2 | 0.793 1 | 0.853 6 | 0.943 6 |
ResU-Net | 0.934 1 | 0.934 8 | 0.832 3 | 0.880 5 | 0.954 3 | |
AttU-Net | 0.953 1 | 0.983 9 | 0.829 9 | 0.900 3 | 0.968 9 | |
CS-Net | 0.958 7 | 0.984 9 | 0.834 8 | 0.903 6 | 0.975 7 | |
MFEU-Net | 0.960 1 | 0.987 6 | 0.838 7 | 0.907 1 | 0.979 1 | |
CHASE_DB1 | U-Net | 0.934 5 | 0.960 4 | 0.802 1 | 0.874 1 | 0.923 6 |
ResU-Net | 0.942 1 | 0.964 5 | 0.820 4 | 0.886 6 | 0.938 5 | |
AttU-Net | 0.951 9 | 0.969 9 | 0.817 8 | 0.887 3 | 0.947 8 | |
CS-Net | 0.961 3 | 0.970 8 | 0.819 9 | 0.888 9 | 0.956 3 | |
MFEU-Net | 0.970 2 | 0.989 9 | 0.832 6 | 0.904 4 | 0.971 3 |
算法 | Acc | Pre | Se | F1 | AUC |
---|---|---|---|---|---|
DUNet | 0.954 7 | 0.973 2 | 0.839 3 | 0.901 3 | 0.947 8 |
CE-Net | 0.952 3 | 0.984 3 | 0.826 8 | 0.898 7 | 0.955 6 |
IterNet | 0.959 9 | 0.979 9 | 0.834 2 | 0.901 1 | 0.968 5 |
SA-UNet | 0.958 3 | 0.978 1 | 0.796 2 | 0.877 8 | 0.964 3 |
RV-GAN | 0.957 8 | 0.982 7 | 0.828 4 | 0.897 7 | 0.976 3 |
MFEU-Net | 0.960 1 | 0.987 6 | 0.838 7 | 0.907 1 | 0.979 1 |
Tab. 2 Comparison of the proposed algorithm and advanced algorithms on DRIVE dataset
算法 | Acc | Pre | Se | F1 | AUC |
---|---|---|---|---|---|
DUNet | 0.954 7 | 0.973 2 | 0.839 3 | 0.901 3 | 0.947 8 |
CE-Net | 0.952 3 | 0.984 3 | 0.826 8 | 0.898 7 | 0.955 6 |
IterNet | 0.959 9 | 0.979 9 | 0.834 2 | 0.901 1 | 0.968 5 |
SA-UNet | 0.958 3 | 0.978 1 | 0.796 2 | 0.877 8 | 0.964 3 |
RV-GAN | 0.957 8 | 0.982 7 | 0.828 4 | 0.897 7 | 0.976 3 |
MFEU-Net | 0.960 1 | 0.987 6 | 0.838 7 | 0.907 1 | 0.979 1 |
算法 | Acc | Pre | Se | F1 | AUC |
---|---|---|---|---|---|
R2U-Net | 0.952 1 | 0.987 2 | 0.799 5 | 0.883 4 | 0.962 5 |
VGN | 0.968 1 | 0.987 8 | 0.799 3 | 0.883 6 | 0.961 3 |
DUNet | 0.956 6 | 0.972 2 | 0.831 2 | 0.896 1 | 0.969 9 |
SA-UNet | 0.967 2 | 0.991 2 | 0.824 9 | 0.900 4 | 0.967 9 |
RV-GAN | 0.964 1 | 0.987 8 | 0.832 4 | 0.903 4 | 0.967 2 |
MFEU-Net | 0.970 2 | 0.989 9 | 0.832 6 | 0.904 4 | 0.971 3 |
Tab. 3 Comparison of the proposed algorithm and advanced algorithms on CHASE_DB1 dataset
算法 | Acc | Pre | Se | F1 | AUC |
---|---|---|---|---|---|
R2U-Net | 0.952 1 | 0.987 2 | 0.799 5 | 0.883 4 | 0.962 5 |
VGN | 0.968 1 | 0.987 8 | 0.799 3 | 0.883 6 | 0.961 3 |
DUNet | 0.956 6 | 0.972 2 | 0.831 2 | 0.896 1 | 0.969 9 |
SA-UNet | 0.967 2 | 0.991 2 | 0.824 9 | 0.900 4 | 0.967 9 |
RV-GAN | 0.964 1 | 0.987 8 | 0.832 4 | 0.903 4 | 0.967 2 |
MFEU-Net | 0.970 2 | 0.989 9 | 0.832 6 | 0.904 4 | 0.971 3 |
数据集 | 算法 | 模块 | 内存/MB | 运行 时间/ms | ||
---|---|---|---|---|---|---|
FIE-RM | MDAC | MCE | ||||
DRIVE | M1 | 967 | 492 | |||
M2 | √ | 989 | 522 | |||
M3 | √ | √ | 1 025 | 534 | ||
M4 | √ | √ | √ | 1 046 | 543 | |
CS-Net | 1 034 | 537 | ||||
CE-Net | 1 056 | 579 | ||||
CHASE_DB1 | M1 | 1 369 | 612 | |||
M2 | √ | 1 398 | 629 | |||
M3 | √ | √ | 1 438 | 647 | ||
M4 | √ | √ | √ | 1 485 | 657 | |
CS-Net | 1 478 | 653 | ||||
CE-Net | 1 471 | 661 |
Tab. 4 Comparison of memory usage and running time of different algorithms
数据集 | 算法 | 模块 | 内存/MB | 运行 时间/ms | ||
---|---|---|---|---|---|---|
FIE-RM | MDAC | MCE | ||||
DRIVE | M1 | 967 | 492 | |||
M2 | √ | 989 | 522 | |||
M3 | √ | √ | 1 025 | 534 | ||
M4 | √ | √ | √ | 1 046 | 543 | |
CS-Net | 1 034 | 537 | ||||
CE-Net | 1 056 | 579 | ||||
CHASE_DB1 | M1 | 1 369 | 612 | |||
M2 | √ | 1 398 | 629 | |||
M3 | √ | √ | 1 438 | 647 | ||
M4 | √ | √ | √ | 1 485 | 657 | |
CS-Net | 1 478 | 653 | ||||
CE-Net | 1 471 | 661 |
数据集 | 算法 | Acc | Pre | Se | F1 | AUC |
---|---|---|---|---|---|---|
DRIVE | M1 | 0.922 3 | 0.924 2 | 0.793 1 | 0.853 6 | 0.950 6 |
M2 | 0.934 8 | 0.945 7 | 0.804 1 | 0.869 1 | 0.953 7 | |
M3 | 0.950 1 | 0.970 2 | 0.830 3 | 0.894 8 | 0.970 6 | |
M4 | 0.960 1 | 0.987 6 | 0.838 7 | 0.907 1 | 0.979 1 | |
CHASE_DB1 | M1 | 0.934 5 | 0.950 4 | 0.807 1 | 0.872 9 | 0.943 6 |
M2 | 0.940 8 | 0.966 5 | 0.815 4 | 0.884 5 | 0.949 7 | |
M3 | 0.955 7 | 0.974 9 | 0.821 4 | 0.891 5 | 0.957 8 | |
M4 | 0.970 2 | 0.989 9 | 0.832 6 | 0.904 4 | 0.971 3 |
Tab. 5 Ablation experimental results
数据集 | 算法 | Acc | Pre | Se | F1 | AUC |
---|---|---|---|---|---|---|
DRIVE | M1 | 0.922 3 | 0.924 2 | 0.793 1 | 0.853 6 | 0.950 6 |
M2 | 0.934 8 | 0.945 7 | 0.804 1 | 0.869 1 | 0.953 7 | |
M3 | 0.950 1 | 0.970 2 | 0.830 3 | 0.894 8 | 0.970 6 | |
M4 | 0.960 1 | 0.987 6 | 0.838 7 | 0.907 1 | 0.979 1 | |
CHASE_DB1 | M1 | 0.934 5 | 0.950 4 | 0.807 1 | 0.872 9 | 0.943 6 |
M2 | 0.940 8 | 0.966 5 | 0.815 4 | 0.884 5 | 0.949 7 | |
M3 | 0.955 7 | 0.974 9 | 0.821 4 | 0.891 5 | 0.957 8 | |
M4 | 0.970 2 | 0.989 9 | 0.832 6 | 0.904 4 | 0.971 3 |
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