AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Journal of Computer Science and Technology Article
Article Link
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Regular Paper

PCRTAM-Net: A Novel Pre-Activated Convolution Residual and Triple Attention Mechanism Network for Retinal Vessel Segmentation

Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin 541004, China
School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, China
Department of Pathology, Ganzhou Municipal Hospital, Ganzhou 341000, China

Idowu Paul Okuwobi worked on the investigation and writing and provided computing power, and Bing-Bing Li was responsible for the data curation and methodology.

Show Author Information

Abstract

Retinal images play an essential role in the early diagnosis of ophthalmic diseases. Automatic segmentation of retinal vessels in color fundus images is challenging due to the morphological differences between the retinal vessels and the low-contrast background. At the same time, automated models struggle to capture representative and discriminative retinal vascular features. To fully utilize the structural information of the retinal blood vessels, we propose a novel deep learning network called Pre-Activated Convolution Residual and Triple Attention Mechanism Network (PCRTAM-Net). PCRTAM-Net uses the pre-activated dropout convolution residual method to improve the feature learning ability of the network. In addition, the residual atrous convolution spatial pyramid is integrated into both ends of the network encoder to extract multiscale information and improve blood vessel information flow. A triple attention mechanism is proposed to extract the structural information between vessel contexts and to learn long-range feature dependencies. We evaluate the proposed PCRTAM-Net on four publicly available datasets, DRIVE, CHASE_DB1, STARE, and HRF. Our model achieves state-of-the-art performance of 97.10%, 97.70%, 97.68%, and 97.14% for ACC and 83.05%, 82.26%, 84.64%, and 81.16% for F1, respectively.

Keywords

residual networkretinal image segmentationtriple attention mechanismatrous convolution

References

[1]
Li L Z, Verma M, Nakashima Y, Nagahara H, Kawasaki R. IterNet: Retinal image segmentation utilizing structural redundancy in vessel networks. In Proc. the 2020 IEEE Winter Conference on Applications of Computer Vision, Mar. 2020, pp.3645-3654. DOI: 10.1109/WACV45572.2020.9093621.
Crossref
[2]

Li Z Y, Zhang X F, Muller H, Zhang S T. Large-scale retrieval for medical image analytics: A comprehensive review. Medical Image Analysis, 2018, 43: 66–84. DOI: 10.1016/j.media.2017.09.007.
Crossref Google Scholar
[3]
Huang K, Yan M. A region based algorithm for vessel detection in retinal images. In Proc. the 9th International Conference on Medical Image Computing and Computer-Assisted Intervention, Oct. 2006, pp.645-653. DOI: 10.1007/11866565_79.
Crossref
[4]

Hoover A D, Kouznetsova V, Goldbaum M. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Medical Imaging, 2002, 19(3): 203–210. DOI: 10.1109/42.845178.
Crossref Google Scholar
[5]

Liskowski P, Krawiec K. Segmenting retinal blood vessels with deep neural networks. IEEE Trans. Medical Imaging, 2016, 35(11): 2369–2380. DOI: 10.1109/TMI.2016.2546227.
Crossref Google Scholar
[6]
Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. In Proc. the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Oct. 2015, pp.234-241. DOI: 10.1007/978-3-319-24574-4_28.
Crossref
[7]

Staal J, Abramoff M D, Niemeijer M, Viergever M A, Van Ginneken B. Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Medical Imaging, 2004, 23(4): 501–509. DOI: 10.1109/TMI.2004.825627.
Crossref Google Scholar
[8]

Orlando J I, Prokofyeva E, Blaschko M B. A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images. IEEE Trans. Biomedical Engineering, 2017, 64(1): 16–27. DOI: 10.1109/TBME.2016.2535311.
Crossref Google Scholar
[9]

Sheng B, Li P, Mo S J, Li H T, Hou X H, Wu Q, Qin J, Fang R G, Feng D D. Retinal vessel segmentation using minimum spanning superpixel tree detector. IEEE Trans. Cybernetics, 2018, 49(7): 2707–2719. DOI: 10.1109/TCYB.2018.2833963.
Crossref Google Scholar
[10]

Yin B J, Li H T, Sheng B, Hou X H, Chen Y, Wu W, Li P, Shen R M, Bao Y Q, Jia W P. Vessel extraction from non-fluorescein fundus images using orientation-aware detector. Medical Image Analysis, 2015, 26(1): 232–242. DOI: 10.1016/j.media.2015.09.002.
Crossref Google Scholar
[11]

Dai L, Wu L, Li H T, Cai C, Wu Q, Kong H Y, Liu R H, Wang X N, Hou X H, Liu Y X, Long X X, Wen Y, Lu L N, Shen Y X, Chen Y, Shen D G, Yang X K, Zou H D, Sheng B, Jia W P. A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nature Communications, 2021, 12: Article No. 3242. DOI: 10.1038/s41467-021-23458-5.
Crossref Google Scholar
[12]

Wang D Y, Haytham A, Pottenburgh J, Saeedi O, Tao Y. Hard attention net for automatic retinal vessel segmentation. IEEE Journal of Biomedical and Health Informatics, 2020, 24(12): 3384–3396. DOI: 10.1109/JBHI.2020.3002985.
Crossref Google Scholar
[13]

Sun M Y, Li K Q, Qi X Q, Dang H, Zhang G H. Contextual information enhanced convolutional neural networks for retinal vessel segmentation in color fundus images. Journal of Visual Communication and Image Representation, 2021, 77: 103134. DOI: 10.1016/j.jvcir.2021.103134.
Crossref Google Scholar
[14]

Jin Q G, Meng Z P, Pham T D, Chen Q, Wei L Y, Su R. DUNet: A deformable network for retinal vessel segmentation. Knowledge-Based Systems, 2019, 178: 149–162. DOI: 10.1016/j.knosys.2019.04.025.
Crossref Google Scholar
[15]

Mou L, Chen L, Cheng J, Gu Z W, Zhao Y T, Liu J. Dense dilated network with probability regularized walk for vessel detection. IEEE Trans. Medical Imaging, 2020, 39(5): 1392–1403. DOI: 10.1109/TMI.2019.2950051.
Crossref Google Scholar
[16]

Wei J H, Zhu G J, Fan Z, Liu J C, Rong Y B, Mo J J, Li W J, Chen X J. Genetic U-Net: Automatically designed deep networks for retinal vessel segmentation using a genetic algorithm. IEEE Trans. Medical Imaging, 2022, 41(2): 292–307. DOI: 10.1109/TMI.2021.3111679.
Crossref Google Scholar
[17]
Fu J, Liu J, Tian H J, Li Y, Bao Y J, Fang Z W, Lu H Q. Dual attention network for scene segmentation. In Proc. the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun. 2019, pp.3141-3149. DOI: 10.1109/CVPR.2019.00326.
Crossref
[18]
Yang Q, Ma B Q, Cui H, Ma J Q. AMF-NET: Attention-aware multi-scale fusion network for retinal vessel segmentation. In Proc. the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, Nov. 2021, pp.3277-3280. DOI: 10.1109/EMBC46164.2021.9630756.
Crossref
[19]
Wu T F, Li L L, Li J B. MSCAN: Multi-scale channel attention for fundus retinal vessel segmentation. In Proc. the 2nd IEEE International Conference on Power Data Science, Dec. 2020, pp.18-27. DOI: 10.1109/ICPDS51559.2020.9332494.
Crossref
[20]

Wu H S, Wang W, Zhong J F, Lei B Y, Wen Z K, Qin J. SCS-Net: A scale and context sensitive network for retinal vessel segmentation. Medical Image Analysis, 2021, 70: 102025. DOI: 10.1016/j.media.2021.102025.
Crossref Google Scholar
[21]
Glorot X, Bordes A, Bengio Y. Deep sparse rectifier neural networks. In Proc. the 14th International Conference on Artificial Intelligence and Statistics, Apr. 2011, pp.315-323.
[22]
Huang G, Liu Z, Van Der Maaten L, Weinberger K Q. Densely connected convolutional networks. In Proc. the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Jul. 2017, pp.2261-2269. DOI: 10.1109/CVPR.2017.243.
Crossref
[23]
He K M, Zhang X Y, Ren S Q, Sun J. Deep residual learning for image recognition. In Proc. the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Jun. 2016, pp.770-778. DOI: 10.1109/CVPR.2016.90.
Crossref
[24]
Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proc. the 32nd International Conference on Machine Learning, Jul. 2015, pp.448-456.
[25]

Mou L, Zhao Y T, Fu H Z, Liu Y H, Cheng J, Zheng Y L, Su P, Yang J L, Chen L, Frangi A F, Akiba M, Liu J. CS2-Net: Deep learning segmentation of curvilinear structures in medical imaging. Medical Image Analysis, 2021, 67: 101874. DOI: 10.1016/j.media.2020.101874.
Crossref Google Scholar
[26]

Shi Z J, Wang T Y, Huang Z, Xie F, Liu Z H, Wang B L, Xu J. MD-Net: A multi-scale dense network for retinal vessel segmentation. Biomedical Signal Processing and Control, 2021, 70: 102977. DOI: 10.1016/j.bspc.2021.102977.
Crossref Google Scholar
[27]

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 10-year-old children: Validation of the computer-assisted image analysis of the retina (CAIAR) program. Investigative Ophthalmology & Visual Science, 2009, 50(5): 2004–2010. DOI: 10.1167/iovs.08-3018.
Crossref Google Scholar
[28]
Köhler T, Budai A, Kraus M F, Odstrčilik J, Michelson G, Hornegger J. Automatic no-reference quality assessment for retinal fundus images using vessel segmentation. In Proc. the 26th IEEE International Symposium on Computer-Based Medical Systems, Jun. 2013, pp.95-100. DOI: 10.1109/CBMS.2013.6627771.
Crossref
[29]
Woo S, Park J, Lee J Y, Kweon I S. CBAM: Convolutional block attention module. In Proc. the 15th European Conference on Computer Vision, Sept. 2018, pp.3-19. DOI: 10.1007/978-3-030-01234-2_1.
Crossref
[30]

Zhuo Z S, Huang J P, Lu K, Pan D R, Feng S T. A size-invariant convolutional network with dense connectivity applied to retinal vessel segmentation measured by a unique index. Computer Methods and Programs in Biomedicine, 2020, 196: 105508. DOI: 10.1016/j.cmpb.2020.105508.
Crossref Google Scholar
[31]

Wu Y C, Xia Y, Song Y, Zhang Y N, Cai W D. NFN+: A novel network followed network for retinal vessel segmentation. Neural Networks, 2020, 126: 153–162. DOI: 10.1016/j.neunet.2020.02.018.
Crossref Google Scholar
[32]

Khan T M, Khan M A U, Rehman N U, Naveed K, Afridi I U, Naqvi S S, Raazak I. Width-wise vessel bifurcation for improved retinal vessel segmentation. Biomedical Signal Processing and Control, 2022, 71: 103169. DOI: 10.1016/j.bspc.2021.103169.
Crossref Google Scholar
[33]

Li Q L, Feng B W, Xie L P, Liang P, Zhang H S, Wang T F. A cross-modality learning approach for vessel segmentation in retinal images. IEEE Trans. Medical Imaging, 2016, 35(1): 109–118. DOI: 10.1109/TMI.2015.2457891.
Crossref Google Scholar
[34]
Wu Y C, Xia Y, Song Y, Zhang Y N, Cai W D. Multiscale network followed network model for retinal vessel segmentation. In Proc. the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, Sept. 2018, pp.119-126.
Crossref
[35]

Tang P, Liang Q K, Yan X T, Zhang D, Coppola G, Sun W. Multi-proportion channel ensemble model for retinal vessel segmentation. Computers in Biology and Medicine, 2019, 111: 103352. DOI: 10.1016/j.compbiomed.2019.103352.
Crossref Google Scholar
[36]

Dong F F, Wu D Y, Guo C Y, Zhang S T, Yang B L, Gong X Y. CRAUNet: A cascaded residual attention U-Net for retinal vessel segmentation. Computers in Biology and Medicine, 2022, 147: 105651. DOI: 10.1016/j.compbiomed.2022.105651.
Crossref Google Scholar
[37]

Soomro T A, Afifi A J, Gao J B, Hellwich O, Zheng L H, Paul M. Strided fully convolutional neural network for boosting the sensitivity of retinal blood vessels segmentation. Expert Systems with Applications, 2019, 134: 36–52. DOI: 10.1016/j.eswa.2019.05.029.
Crossref Google Scholar
Journal of Computer Science and Technology
Volume 38 Issue 3,
May 2023
Pages 567-581
DOI: 10.1007/s11390-023-3066-4
Cite this article:
Wang H-D, Li Z-Z, Okuwobi IP, et al. PCRTAM-Net: A Novel Pre-Activated Convolution Residual and Triple Attention Mechanism Network for Retinal Vessel Segmentation. Journal of Computer Science and Technology, 2023, 38(3): 567-581. https://doi.org/10.1007/s11390-023-3066-4

321

Views

1

Crossref

2

Web of Science

3

Scopus

0

CSCD

Altmetrics
Received: 30 December 2022
Accepted: 22 May 2023
Published: 30 May 2023
© Institute of Computing Technology, Chinese Academy of Sciences 2023
Return