Top

Neural Computing and Applications

Published in:

14-03-2022 | Original Article

Res2Unet: A multi-scale channel attention network for retinal vessel segmentation

Authors: Xuejian Li, Jiaqi Ding, Jijun Tang, Fei Guo

Published in: Neural Computing and Applications | Issue 14/2022

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Retinal diseases can be found timely by observing retinal fundus images. So extracting blood vessels from retinal images is an important part because it is the way to show the changes of vessels. However, most of the previous methods based on deep learning cared more about accuracy and ignored the complexity of the model for segmenting retinal vessels, which makes these methods difficult to apply to medical equipment. Besides, due to the great differences in the width of retinal vessels, some methods cannot well-extract all blood vessels at the same time. Based on above limitations, we propose a new lightweight network, called Res2Unet. It applies a multi-scale strategy to extract blood vessels of different widths and integrates the strategy into the channels to greatly reduce parameters and computation resources. Res2Unet also uses channel-attention mechanism to promote the communication between channels and recalibrate the relationship of channel features. Then, we propose two post-processing methods. One called the local threshold method(LTM) uses a lower local threshold to excavate hidden blood vessels in discontinuous blood vessels of the probability maps. The other named weighted correction method (WCM) combines the probability maps of Unet and Res2Unet to remove false positive and false negative samples. On the DRIVE dataset, the Dice, IOU and AUC of our Res2Unet reach 0.8186, 0.6926 and 0.9772, respectively, which are better than that of Unet with 0.8109, 0.6817 and 0.9751. Importantly, the number of parameters of Res2Unet are about one-third of Unet. It means that Res2Unet has less hardware requirements.

previous article Intuitionistic fuzzy inference system with weighted comprehensive evaluation considering standard deviation-cosine entropy: a fused forecasting model

next article RESC: REfine the SCore with adaptive transformer head for end-to-end object detection

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Aguiree F, Brown A, Cho NH, Dahlquist G, Dodd S, Dunning T, Hirst M, Hwang C, Magliano D, Patterson C (2013) Idf diabetes atlas : sixth edition. International Diabetes Federation

Salazar-Gonzalez A, Kaba D, Li Y, Liu X (2014) Segmentation of the blood vessels and optic disk in retinal images. IEEE J Biomed Health Infor 18(6):1874–1886CrossRef

Annunziata R, Garzelli A, Ballerini L, Mecocci A, Trucco E (2016) Leveraging multiscale hessian-based enhancement with a novel exudate inpainting technique for retinal vessel segmentation. IEEE J Biomed Health Info 20(4):1129–1138CrossRef

Orlando JI, Prokofyeva E, Blaschko MB (2016) A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images. IEEE Transact Biomed Eng 64(1):16–27CrossRef

Zhang Y, Chung ACS (2018) Deep supervision with additional labels for retinal vessel segmentation task. In: Medical image computing and computer assisted intervention – MICCAI 2018, pages 83–91. Springer, Cham

Filipe MOA, Rafael MPS, Alberto BSC (2018) Retinal vessel segmentation based on fully convolutional neural networks. Expert Sys Appl 112:229–242CrossRef

Wu Y, Xia Y, Song Y, Zhang Y, Cai W(2018) Multiscale network followed network model for retinal vessel segmentation. In Medical image computing and computer assisted intervention – MICCAI 2018, pages 119–126

Li Q, Feng B, Xie LP, Liang P, Zhang H, Wang T (2015) A cross-modality learning approach for vessel segmentation in retinal images. IEEE Trans Med Imag 35(1):109–118CrossRef

Sheng B, Li P, Mo S, Li H, Hou X, Wu Q, Qin J, Fang R, Feng DD (2019) Retinal vessel segmentation using minimum spanning superpixel tree detector. IEEE Trans Cybern 49(7):2707–2719CrossRef

10.

Rodrigues EO, Conci A, Liatsis P (2020) Element: multi-modal retinal vessel segmentation based on a coupled region growing and machine learning approach. IEEE J Biomed Health Info 24(12):3507–3519CrossRef

11.

Ma Y, Hao H, Xie J, Fu H, Zhang J, Yang J, Wang Z, Liu J, Zheng Y, Zhao Y (2021) Rose: a retinal oct-angiography vessel segmentation dataset and new model. IEEE Transact Med Imag 40(3):928–939CrossRef

12.

Wang D, Haytham A, Pottenburgh J, Saeedi O, Tao Y (2020) Hard attention net for automatic retinal vessel segmentation. IEEE J Biomed Health Info 24(12):3384–3396CrossRef

13.

Li X, Jiang Y, Li M, Yin S (2021) Lightweight attention convolutional neural network for retinal vessel image segmentation. IEEE Transact Ind Info 17(3):1958–1967CrossRef

14.

Lv Y, Ma H, Li J, Liu S (2020) Attention guided u-net with atrous convolution for accurate retinal vessels segmentation. IEEE Access 8:32826–32839CrossRef

15.

Fu Q, Li S, Wang X (2020) Mscnn-am: a multi-scale convolutional neural network with attention mechanisms for retinal vessel segmentation. IEEE Access 8:163926–163936CrossRef

16.

Zhang S,Fu H, Yan Y, Zhang Y, Wu Q, Yang M, Tan M, Xu Y 2019) Attention guided network for retinal image segmentation. In:Medical image computing and computer assisted intervention – MICCAI 2019, pages 797–805, Springer, Cham

17.

Mou L, Zhao Y, Chen L (2019) Cs-net: channel and spatial attention network for curvilinear structure segmentation. In MICCAI 2019: Medical image computing and computer assisted intervention - MICCAI 2019, pages 721–730, Springer, Cham

18.

Ma W,Yu S, Ma K, Wang J, Ding X, Zheng Y(2019) Multi-task neural networks with spatial activation for retinal vessel segmentation and artery/vein classification. In:International conference on medical image computing and computer-assisted intervention, pages 769–778, Springer, Cham

19.

Li D, Bawany MH, Kuriyan AE, Ramchandran RS, Sharma G (2020) A novel deep learning pipeline for retinal vessel detection in fluorescein angiography. IEEE Transactions on Image Processing, PP(99):1–1

20.

Xie S, Nie H(2013) Retinal vascular image segmentation using genetic algorithm plus fcm clustering. In:2013 Third International conference on intelligent system design and engineering applications, pages 1225–1228, Hong Kong, China. IEEE

21.

Roychowdhury S, Koozekanani DD, Parhi KK (2015) Iterative vessel segmentation of fundus images. IEEE Trans Biomed Eng 62(7):1738–1749CrossRef

22.

Liu B, Gu L, Lu F (2019) Unsupervised ensemble strategy for retinal vessel segmentation. In:Medical image computing and computer assisted intervention – MICCAI 2019, pages 111–119. Springer, Cham

23.

Shah SAA, Shahzad A, Khan MA, Lu C, Tang TB (2019) Unsupervised method for retinal vessel segmentation based on gabor wavelet and multiscale line detector. IEEE Access 7:167221–167228CrossRef

24.

Wu Y, Xia Y, Song Y, Zhang D, Cai W (2019) Vessel-net: retinal vessel segmentation under multi-path supervision. In:International conference on medical image computing and computer-assisted intervention, pages 264–272. Springer International Publishing, Cham

25.

Kamran SA, Hossain KF, Tavakkoli A, Zuckerbrod SL, Sanders KM, Baker SA (2021) RV-GAN: Segmenting retinal vascular structure in fundus photographs using a novel multi-scale generative adversarial network. arXiv e-prints, page arXiv:2101.00535

26.

Zhou Y, Yu H, Shi H (2021) Study group learning: Improving retinal vessel segmentation trained with noisy labels. In:Medical image computing and computer assisted intervention – MICCAI 2020”, pages 57–67. Springer, Cham

27.

Guo C, Szemenyei M, Yi Y, Wang W, Chen B, Fan C (2020) SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation. arXiv e-prints, page arXiv:2004.03696

28.

Li L, Verma M, Nakashima Y, Nagahara H, Kawasaki R (2020) Iternet: retinal image segmentation utilizing structural redundancy in vessel networks. In:2020 IEEE winter conference on applications of computer vision (WACV), pages 3654, Snowmass, CO, USA. IEEE

29.

Zhuang J (2018) LadderNet: multi-path networks based on U-Net for medical image segmentation. arXiv e-prints, page arXiv:1810.07810

30.

Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In:2018 IEEE/CVF conference on computer vision and pattern recognition, pages 4510–4520, Salt Lake City, UT, USA. IEEE

31.

Ma N, Zhang X, Zheng HT, Sun J (2018) Shufflenet v2: Practical guidelines for efficient cnn architecture design. In :Computer vision – ECCV 2018, pages 122–138. Springer, Cham

32.

Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In :2016 IEEE conference on computer vision and pattern recognition (CVPR), pages 4510-4520, Salt Lake City, UT, USA, 2018. IEEE

33.

Gao SH, Cheng MM, Zhao K, Zhang XY, Torr P (2019) Res2net: a new multi-scale backbone architecture. IEEE Transact Patt Anal Mach Intell 43(2):652–662CrossRef

34.

Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In:Medical image computing and computer-assisted intervention – MICCAI 2015

35.

Hu J, Shen L, Albanie S, Sun G, Wu E (2017) Squeeze-and-excitation networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, PP(99):2011–2023

36.

Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proceed IEEE 86(11):2278–2324CrossRef

37.

Krizhevsky A, Sutskever I, Hinton G (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, vol 25

38.

Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint https://arxiv.org/abs/1409.1556

39.

Long Jonathan, Shelhamer Evan, Darrell Trevor (2015) Fully convolutional networks for semantic segmentation. IEEE Trans Patt Anal Mach Intell 39(4):640–651

40.

Zhang Z, Fu H, Dai H, Shen J, Shao L (2019) Et-net: a generic edge-attention guidance network for medical image segmentation. In:Medical image computing and computer assisted intervention – MICCAI 2019”

41.

Xiao X, Lian S, Luo Z, Li S (2018) Weighted res-unet for high-quality retina vessel segmentation. In 2018 9th International conference on information technology in medicine and education (ITME), pages 327–331, Las Vegas, NV, USA. IEEE

42.

Alom MZ, Hasan M, Yakopcic C, Taha TM, Asari VK (2018) Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for medical image segmentation. arXiv e-prints, page arXiv:1802.06955

43.

Mou Lei, Chen Li, Cheng Jun, Zaiwang Gu, Zhao Yitian, Liu Jiang (2020) Dense dilated network with probability regularized walk for vessel detection. IEEE Transact Medical Imag 39(5):1392–1403CrossRef

44.

Szegedy C, Liu W, Jia Y, Sermanet P, Rabinovich A (2014) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

45.

He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In 2016 IEEE conference on computer vision and pattern recognition (CVPR), pages 770–778, Las Vegas, NV, USA. IEEE

46.

Jha D, Smedsrud PH, Riegler MA, Johansen D, Lange TD, Halvorsen P, Johansen HD (2019) Resunet++: An advanced architecture for medical image segmentation. In 2019 IEEE international symposium on multimedia (ISM), pages 225–2255, San Diego, CA, USA. IEEE

47.

Tang Z, Liu X, Li Y, Yap P, Shen D (2020) Multi-atlas brain parcellation using squeeze-and-excitation fully convolutional networks. IEEE Transact Image Process 29:6864–6872CrossRef

48.

Marín D, Aquino A, Gegundez-Arias ME, Bravo JM (2011) A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Transact Med Imag 30(1):146–158CrossRef

49.

Dasgupta Avijit, Singh Sonam (2017) A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation. In 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017), pages 248–251, Melbourne, VIC, Australia. IEEE

50.

Zhou Z, Mmr Siddiquee, Tajbakhsh N, Liang J (2020) Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Transact Med Imag 39(6):1856–1867CrossRef

51.

Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B, Glocker B, Rueckert D (2018) Attention U-Net: Learning Where to Look for the Pancreas. arXiv e-prints, page arXiv:1804.03999

52.

Zaiwang Gu, Cheng Jun, Huazhu Fu, Zhou Kang, Hao Huaying, Zhao Yitian, Zhang Tianyang, Gao Shenghua, Liu Jiang (2019) Ce-net: context encoder network for 2d medical image segmentation. IEEE Transact Med Imag 38(10):2281–2292CrossRef

53.

Azzopardi G, Strisciuglio N, Vento M, Petkov N (2015) Trainable cosfire filters for vessel delineation with application to retinal images. Med Image Anal 19(1):46–57CrossRef

54.

Zhao Y, Rada L, Chen K, Harding SP, Zheng Y (2015) Automated vessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images. IEEE Transact Med Imag 34(9):1797–1807CrossRef

55.

Zhou L, Qi Y, Xun X, Yun G, Yang J (2017) Improving dense conditional random field for retinal vessel segmentation by discriminative feature learning and thin-vessel enhancement. Comput Method Program Biomed 148:13–25CrossRef

56.

Fraz MM, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka AR, Owen CG, Barman SA (2012) An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Transact Biomed Eng 59(9):2538–2548CrossRef

57.

Yan Z, Yang X, Cheng KT (2018) Joint segment-level and pixel-wise losses for deep learning based retinal vessel segmentation. IEEE Transact Biomed Eng 65(9):1912–1923CrossRef

58.

Yan Zengqiang, Yang Xin, Cheng Kwang-Ting (2019) A three-stage deep learning model for accurate retinal vessel segmentation. IEEE J Biomed Health Info 23(4):1427–1436CrossRef

59.

Sreejini KS, Govindan VK (2015) Improved multiscale matched filter for retina vessel segmentation using PSO algorithm. Egypt Info J 16(3):253–260

60.

Kumar K, Samal D, Suraj (2020) Automated retinal vessel segmentation based on morphological preprocessing and 2d-gabor wavelets. In:Advanced computing and intelligent engineering, pages 411-423 2020. Springer, Singapore

Title: Res2Unet: A multi-scale channel attention network for retinal vessel segmentation
Authors: Xuejian Li
Jiaqi Ding
Jijun Tang
Fei Guo
Publication date: 14-03-2022
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 14/2022
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-022-07086-8

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 14/2022

Polynomial dendritic neural networks

Heterogeneous face quality assessment

Self-adaptive differential evolution with Gaussian–Cauchy mutation for large-scale CHP economic dispatch problem

TF-SOD: a novel transformer framework for salient object detection

A two-stage energy management framework for optimal scheduling of multi-microgrids with generation and demand forecasting

Zero root-mean-square error for single- and double-diode photovoltaic models parameter determination

Premium Partner