JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2021, Vol. 56 ›› Issue (9): 13-20.doi: 10.6040/j.issn.1671-9352.0.2020.655

Previous Articles     Next Articles

ASR-UNet: an improved retinal vessels segmentation algorithm based on attention mechanism

San-li YI1,2(),Jian-ting CHEN1,Jian-feng HE1,*()   

  1. 1. School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
    2. Key Laboratory of Computer Technology Application of Yunnan Province, Kunming 650500, Yunnan, China
  • Received:2020-11-24 Online:2021-09-20 Published:2021-09-13
  • Contact: Jian-feng HE E-mail:152514845@qq.com;120112624@qq.com

RichHTML

173

PDF (PC)

704

Abstract:

Factors such as tiny blood vessels in the retina and light will cause the problem of low segmentation accuracy of existing algorithms. This paper proposes a new retinal vessel segmentation algorithm model based on U-Net segmentation model combined with attention mechanism. The improved measures of this paper are as follows: first, we use SE-Resnet to replace the common convolution used in U-Net, thereby introducing a channel attention mechanism to enhance the original features. After that, AG are added to the jump connection part to enhance the spatial characteristics, thereby improving the models ability to segment the microstructure of blood vessels. The algorithm of this paper is verified on the public datasets DRIVE and CHASE_DB1, and the results are 0.9697 and 0.9657 on Acc, 0.8044 and 0.7673 on sensitivity, and 0.9859 and 0.9866 on specificity. Experimental results show that the proposed algorithm has better performance than recent retinal blood vessel segmentation algorithms.

Key words: image segmentation, U-Net, retinal vessel segmentation

CLC Number: 

  • TP391

Fig.1

SE module structure"

Fig.2

SE-Resnet module structure"

Fig.3

Attention gate module"

Fig.4

Improved retinal vessel segmentation algorithm model of ASR-UNet"

Fig.5

Retinal image channel contrast"

Fig.6

Patches"

Table 1

Evaluation index and formula"

Evaluation metric Description
Accuracy(Acc) $ \frac{{TP + TN}}{{TP + TN + FP + FN}} $
Sensitivity(Sn) $ \frac{{TP}}{{TP + FN}} $
Specificity(Sp) $ \frac{{TN}}{{TN + FP}} $
F1-score(F1) $ \frac{{2 \times TP}}{{2 \times TP + FP + FN}} $

Fig.7

Segmentation on DRIVE test dataset"

Table 2

Performance metrics for different algorithms in DRIVE database"

方法出处 年份 Acc Sn Sp F1
Vega[21] 2015 0.9412 0.7444 0.9612 0.6884
Liskowski[22] 2016 0.9535 0.7811 0.9807 N/A
Wu[14] 2018 0.9567 0.7844 0.9847 N/A
Alom[23] 2019 0.9556 0.7792 0.9813 0.8171
Zhun Fan[15] 2019 0.9661 0.7957 0.9827 0.8033
Our method 2020 0.9697 0.8044 0.9859 0.8256

Table 3

Performance metrics for different algorithms in CHASE_DB1 database"

方法出处 年份 Acc Sn Sp F1
Roychowdhury[24] 2015 0.9467 0.7615 0.9575 N/A
Liskowski[22] 2016 0.9628 0.7816 0.9836 N/A
Li[25] 2016 0.9527 0.7569 0.9816 N/A
Wu[14] 2018 0.9631 0.7538 0.9847 N/A
Alom[23] 2019 0.9634 0.7756 0.9820 0.7928
Our method 2020 0.9657 0.7673 0.9866 0.8105

Table 4

Performance metrics for different algorithms in DRIVE test database"

Model Acc Sn Sp F1
U-Net 0.9688 0.7915 0.9861 0.8186
U-Net+AGs 0.9689 0.7911 0.9862 0.8189
U-Net+SE-Resnet 0.9695 0.7758 0.9883 0.8197
Our method 0.9697 0.8044 0.9859 0.8256
1 KIRBAS C , QUEK F . A review of vessel extraction techniques and algorithms[J]. ACM Computing Surveys (CSUR), 2004, 36 (2): 81- 121.
doi: 10.1145/1031120.1031121
2 LIU I , SUN Y . Recursive tracking of vascular networks in angiograms based on the detection-deletion scheme[J]. IEEE Transactions on Medical Imaging, 1993, 12 (2): 334- 341.
doi: 10.1109/42.232264
3 CAN A , SHEN H , TURNER J N , et al. Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms[J]. IEEE Transactions on Information Technology in Biomedicine, 1999, 3 (2): 125- 138.
doi: 10.1109/4233.767088
4 ZANA F , KLEIN J C . Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation[J]. IEEE Transactions on Image Processing, 2001, 10 (7): 1010- 1019.
doi: 10.1109/83.931095
5 MENDONCA A M , CAMPILHO A . Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction[J]. IEEE Transactions on Medical Imaging, 2006, 25 (9): 1200- 1213.
doi: 10.1109/TMI.2006.879955
6 CHAUDHURI S , CHATTERJEE S , KATZ N , et al. Detection of blood vessels in retinal images using two-dimensional matched filters[J]. IEEE Transactions on Medical Imaging, 1989, 8 (3): 263- 269.
doi: 10.1109/42.34715
7 HOOVER A D , KOUZNETSOVA V , GOLDBAUM M . Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response[J]. IEEE Transactions on Medical Imaging, 2000, 19 (3): 203- 210.
doi: 10.1109/42.845178
8 KASS M , WITKIN A , TERZOPOULOS D . Snakes: active contour models[J]. International Journal of Computer Vision, 1988, 1 (4): 321- 331.
doi: 10.1007/BF00133570
9 ESPONA L, CARREIRA M J, ORTEGA M, et al. A snake for retinal vessel segmentation[C]//Iberian Conference on Pattern Recognition and Image Analysis. Berlin: Springer, 2007: 178-185.
10 STAAL J , ABRMOFF M D , NIEMEIJER M , et al. Ridge-based vessel segmentation in color images of the retina[J]. IEEE Transactions on Medical Imaging, 2004, 23 (4): 501- 509.
doi: 10.1109/TMI.2004.825627
11 RICCI E , PERFETTI R . Retinal blood vessel segmentation using line operators and support vector classification[J]. IEEE Transactions on Medical Imaging, 2007, 26 (10): 1357- 1365.
doi: 10.1109/TMI.2007.898551
12 FRAZ M M, REMAGNINO P, HOPPE A, et al. A supervised method for retinal blood vessel segmentation using line strength, multiscale Gabor and morphological features[C]//2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA). [S. l. ]: IEEE, 2011: 410-415.
13 RONNEBERGER O, FISCHER P, BROX T. U-net: Convolutional networks for biomedical image segmentation[C]//International Conference on Medical Image Computing and Computer-assisted Intervention. Cham: Springer, 2015: 234-241.
14 WU Y, XIA Y, SONG Y, et al. Multiscale network followed network model for retinal vessel segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2018: 119-126.
15 FAN Z, MO J, QIU B, et al. Accurate retinal vessel segmentation via octave convolution neural network[EB/OL]. (2019-06-28)[2020-05-18]. https://arxiv.org/abs/1906.12193v1.
16 KOMATSU R , FUJII H , TAMURA Y , et al. Octave deep plane-sweeping network: reducing spatial redundancy for learning-based plane-sweeping stereo[J]. IEEE Access, 2019, 7, 150306- 150317.
doi: 10.1109/ACCESS.2019.2947195
17 SZEGEDY C, IOFFE S, VANHOUCKE V, et al. Inception-v4, inception-resnet and the impact of residual connections on learning[C]//Proceedings of the Thirty-first AAAI Conference on Artificial Intelligence. [S. l. ]: AAAI, 2017: 4278-4284
18 HU J , SHEN L , SUN G . Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 42 (8): 2011- 2023.
19 OKTAY O, SCHLEMPER J, FOLGOC L L, et al. Attention U-net: learning where to look for the pancreas[J/OL]. (2018-04-13)[2020-05-18]. https://arxiv.org/abs/1804.03999v2.
20 OWEN C G , RUDNICKA A R , MULLEN R , et al. Measuring retinal vessel tortuosity in 10-year-old children: validation of the computer-assisted image analysis of the retina (CAIAR) program[J]. Investigative Ophthalmology & Visual Science, 2009, 50 (5): 2004- 2010.
21 VEGA R , SANCHEZ-ANTE G , FALCON-MORALES L E , et al. Retinal vessel extraction using lattice neural networks with dendritic processing[J]. Computers in Biology and Medicine, 2015, 58, 20- 30.
doi: 10.1016/j.compbiomed.2014.12.016
22 LISKOWSKI P , KRAWIEC K . Segmenting retinal blood vessels with deep neural networks[J]. IEEE Transactions on Medical Imaging, 2016, 35 (11): 2369- 2380.
doi: 10.1109/TMI.2016.2546227
23 ALOM M Z , YAKOPCIC C , HASAN M , et al. Recurrent residual U-net for medical image segmentation[J]. Journal of Medical Imaging, 2019, 6 (1): 014006.
24 ROYCHOWDHURY S , KOOZEKANANI D D , PARHI K K . Iterative vessel segmentation of fundus images[J]. IEEE Transactions on Biomedical Engineering, 2015, 62 (7): 1738- 1749.
doi: 10.1109/TBME.2015.2403295
25 LI Q , FENG B , XIE L P , et al. A cross-modality learning approach for vessel segmentation in retinal images[J]. IEEE Transactions on Medical Imaging, 2015, 35 (1): 109- 118.
[1] LIU Zhan-jie1, MA Ru-ning1, ZOU Guo-ping1, ZHONG Bao-jiang2, DING Jun-di 3. An algorithm for color image segmentation based on region growth [J]. J4, 2010, 45(7): 76-80.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] LIU Ru-jun,CAO Yu-xia,ZHOU Ping . Anti-control for discrete chaos systems by small feedback[J]. J4, 2007, 42(7): 30 -32 .
[2] HU Mo-Yin. Multiplicity results for a class of quasi-linear Neumann problems[J]. J4, 2009, 44(10): 39 -42 .
[3] DU Rui-ying, YANG Yong, CHEN Jing, WANG Chi-heng. An efficient network traffic classification scheme based on similarity[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(09): 109 -114 .
[4] SHI Chang-guang . Multi-soliton solution of the Faddeev model[J]. J4, 2007, 42(7): 38 -40 .
[5] DONG Jiong, CAO Xiao-hong. Weyls theorem for the cube of operator and compact perturbations[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2016, 51(8): 15 -21 .
[6] . Study on the determination method of total flavonoids in ginkgo biloba extract[J]. J4, 2009, 44(5): 40 -44 .
[7] ZHANG Fengxia1,　LI Ying1,2,　GUO Wenbin1, ZHAO Jianli1. Extremal ranks for block Hermitian and skew-Hermitian matrices[J]. J4, 2010, 45(4): 106 -110 .
[8] BI Xiao-dong . Free product of left quasi-normal bands[J]. J4, 2008, 43(6): 83 -86 .
[9] LING Mi-ran, MI Ju-sheng, MA Li. Heterogeneous formal contexts for uncertainty reasoning[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(08): 28 -32 .
[10] WU Song-li1,2, CHEN Gui-you3*. Outer-disturbances and attributes conjunctive shrink#br# law mining and separating[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(06): 1 -5 .
Copyright 2018 © Editorial office of JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE)
Supported by Beijing Magtech Co.Ltd