Skip to main content

Advertisement

SpringerLink
Log in

DPN: detail-preserving network with high resolution representation for efficient segmentation of retinal vessels

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Retinal vessels are important biomarkers for many ophthalmological and cardiovascular diseases. Hence, it is of great significance to develop automatic models for computer-aided diagnosis. Existing methods, such as U-Net follows the encoder-decoder pipeline, where detailed information is lost in the encoder in order to achieve a large field of view. Although spatial detailed information could be recovered partly in the decoder, while there is noise in the high-resolution feature maps of the encoder. And, we argue this encoder-decoder architecture is inefficient for vessel segmentation. In this paper, we present the detail-preserving network (DPN), which avoids the encoder-decoder pipeline. To preserve detailed information and learn structural information simultaneously, we designed the detail-preserving block (DP-Block). Further, we stacked eight DP-Blocks together to form the DPN. More importantly, there are no down-sampling operations among these blocks. Therefore, the DPN could maintain a high/full resolution during processing, avoiding the loss of detailed information. To illustrate the effectiveness of DPN, we conducted experiments over three public datasets. Experimental results show, compared to state-of-the-art methods, DPN shows competitive/better performance in terms of segmentation accuracy, segmentation speed, and model size. Specifically, (1) Our method achieves comparable segmentation performance on the DRIVE, CHASE_DB1, and HRF datasets. (2) The segmentation speed of DPN is over 20-160\(\times\) faster than other methods on the DRIVE dataset. (3) The number of parameters of DPN is around 120k, far less than all comparison methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Azzopardi G, Petkov N (2013) Automatic detection of vascular bifurcations in segmented retinal images using trainable cosfire filters. Pattern Recognit Lett 34(8):922–933. https://doi.org/10.1016/j.patrec.2012.11.002

    Article  Google Scholar 

  • 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):46–57

    Article  Google Scholar 

  • Budai A, Bock R, Maier A, Hornegger J, Michelson G (2013) Robust vessel segmentation in fundus images. Int J Biomed Imaging. https://doi.org/10.1155/2013/154860

    Article  Google Scholar 

  • Cao L, Li H, Zhang Y, Zhang L, Xu L (2020) Hierarchical method for cataract grading based on retinal images using improved haar wavelet. Inf Fusion 53:196–208. https://doi.org/10.1016/j.inffus.2019.06.022

    Article  Google Scholar 

  • Christodoulidis A, Hurtut T, Tahar HB, Cheriet F (2016) A multi-scale tensor voting approach for small retinal vessel segmentation in high resolution fundus images. Comput Med Imaging Graph 52:28–43. https://doi.org/10.1016/j.compmedimag.2016.06.001

    Article  Google Scholar 

  • Fraz MM, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka AR, Owen CG, Barman SA (2012) Blood vessel segmentation methodologies in retinal images—a survey. Comput Methods Programs Biomed 108(1):407–433. https://doi.org/10.1016/j.cmpb.2012.03.009

    Article  Google Scholar 

  • Fu H, Xu Y, Lin S, Wong DWK, Liu J (2016) Deepvessel: retinal vessel segmentation via deep learning and conditional random field. In: International conference on medical image computing and computer-assisted intervention, pp 132–139, https://doi.org/10.1007/978-3-319-46723-8_16

  • Garg S, Sivaswamy J, Chandra S (2007) Unsupervised curvature-based retinal vessel segmentation. In: IEEE international symposium on biomedical imaging: from nano to macro, IEEE, pp 344–347, https://doi.org/10.1109/ISBI.2007.356859

  • Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: International conference on artificial intelligence and statistic (AISTATS), PMLR, Proceedings of machine learning research, vol 9, pp 249–256

  • Guo S, Wang K, Kang H, Zhang Y, Gao Y, Li T (2019) Bts-dsn: deeply supervised neural network with short connections for retinal vessel segmentation. Int J Med Inform 126:105–113. https://doi.org/10.1016/j.ijmedinf.2019.03.015

    Article  Google Scholar 

  • Irshad S, Akram MU (2014) Classification of retinal vessels into arteries and veins for detection of hypertensive retinopathy. In: Cairo international biomedical engineering conference (CIBEC), IEEE, pp 133–136

  • Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: convolutional architecture for fast feature embedding. In: ACM international conference on multimedia, pp 675–678. https://doi.org/10.1145/2647868.2654889

  • Jin Q, Meng Z, Pham TD, Chen Q, Wei L, Su R (2019) Dunet: a deformable network for retinal vessel segmentation. Knowl Based Syst 178:149–162. https://doi.org/10.1016/j.knosys.2019.04.025

    Article  Google Scholar 

  • Kingma DP, Ba JL (2015) Adam: a method for stochastic optimization. In: International conference on learning representations, pp 1–13

  • Lee CY, Xie S, Gallagher P, Zhang Z, Tu Z (2015) Deeply-supervised nets. In: International conference on artificial intelligence and statistics, proceedings of machine learning research, vol 38, pp 562–570. http://proceedings.mlr.press/v38/lee15a.html

  • Li Q, You J, Zhang L, Bhattacharya P (2006) A multiscale approach to retinal vessel segmentation using gabor filters and scale multiplication. IEEE Int Conf Syst Man Cybern 4:3521–3527. https://doi.org/10.1109/ICSMC.2006.384665

    Article  Google Scholar 

  • Liskowski P, Krawiec K (2016) Segmenting retinal blood vessels with deep neural networks. IEEE Trans Med Imaging 35(11):2369–2380. https://doi.org/10.1109/TMI.2016.2546227

    Article  Google Scholar 

  • Maninis KK, Pont-Tuset J, Arbeláez P, Van Gool L (2016) Deep retinal image understanding. In: International conference on medical image computing and computer-assisted intervention, pp 140–148. https://doi.org/10.1007/978-3-319-46723-8_17

  • Mookiah MRK, Hogg S, MacGillivray TJ, Prathiba V, Pradeepa R, Mohan V, Anjana RM, Doney AS, Palmer CN, Trucco E (2020) A review of machine learning methods for retinal blood vessel segmentation and artery/vein classification. Med Image Anal 68:101905

    Article  Google Scholar 

  • Oliveira A, Pereira S, Silva CA (2018) Retinal vessel segmentation based on fully convolutional neural networks. Expert Syst Appl 112:229–242. https://doi.org/10.1016/j.eswa.2018.06.034

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, New York, pp 234–241,. https://doi.org/10.1007/978-3-319-24574-4_28

  • Saleh MD, Eswaran C, Mueen A (2011) An automated blood vessel segmentation algorithm using histogram equalization and automatic threshold selection. J Digit Imaging 24(4):564–572

    Article  Google Scholar 

  • Schmidt-Erfurth U, Sadeghipour A, Gerendas BS, Waldstein SM, Bogunović H (2018) Artificial intelligence in retina. Prog Retin Eye Res 67:1–29. https://doi.org/10.1016/j.preteyeres.2018.07.004

    Article  Google Scholar 

  • Scott IU, Alexandrakis G, Cordahi GJ, Murray TG (1999) Diffuse and circumscribed choroidal hemangiomas in a patient with sturge-weber syndrome. Arch Ophthalmol 117(3):406–407

    Google Scholar 

  • Shelhamer E, Long J, Darrell T (2017) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):640–651. https://doi.org/10.1109/TPAMI.2016.2572683

    Article  Google Scholar 

  • Srinidhi CL, Aparna P, Rajan J (2017) Recent advancements in retinal vessel segmentation. J Med Syst 41(4):70

    Article  Google Scholar 

  • Srinidhi CL, Aparna P, Rajan J (2018) A visual attention guided unsupervised feature learning for robust vessel delineation in retinal images. Biomed Signal Process Control 44:110–126

    Article  Google Scholar 

  • Staal J, Abràmoff MD, Niemeijer M, Viergever MA, Van Ginneken B (2004) Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 23(4):501–509. https://doi.org/10.1109/tmi.2004.825627

    Article  Google Scholar 

  • Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: IEEE conference on computer vision and pattern recognition, pp 1–9. https://doi.org/10.1109/CVPR.2015.7298594

  • Villalobos-Castaldi FM, Felipe-Riverón EM, Sánchez-Fernández LP (2010) A fast, efficient and automated method to extract vessels from fundus images. J Vis 13(3):263–270

    Article  Google Scholar 

  • Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  • Wang Y, Ji G, Lin P, Trucco E (2013) Retinal vessel segmentation using multiwavelet kernels and multiscale hierarchical decomposition. Pattern Recognit 46(8):2117–2133. https://doi.org/10.1016/j.patcog.2012.12.014

    Article  Google Scholar 

  • Wang B, Qiu S, He H (2019a) Dual encoding u-net for retinal vessel segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 84–92. https://doi.org/10.1007/978-3-030-32239-7_10

  • Wang W, Wang W, Hu Z (2019b) Retinal vessel segmentation approach based on corrected morphological transformation and fractal dimension. IET Image Process 13(13):2538–2547

    Article  Google Scholar 

  • Wang X, Jiang X, Ren J (2019c) Blood vessel segmentation from fundus image by a cascade classification framework. Pattern Recognit 88:331–341. https://doi.org/10.1016/j.patcog.2018.11.030

    Article  Google Scholar 

  • Wang J, Sun K, Cheng T, Jiang B, Deng C, Zhao Y, Liu D, Mu Y, Tan M, Wang X et al (2020) Deep high-resolution representation learning for visual recognition. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2020.2983686

    Article  Google Scholar 

  • Wong TY, Coresh J, Klein R, Muntner P, Couper DJ, Sharrett AR, Klein BE, Heiss G, Hubbard LD, Duncan BB (2004) Retinal microvascular abnormalities and renal dysfunction: the atherosclerosis risk in communities study. J Am Soc Nephrol 15(9):2469–2476. https://doi.org/10.1097/01.ASN.0000136133.28194.E4

    Article  Google Scholar 

  • Wong TY, Sun J, Kawasaki R, Ruamviboonsuk P, Gupta N, Lansingh VC, Maia M, Mathenge W, Moreker S, Muqit MM et al (2018) Guidelines on diabetic eye care: the international council of ophthalmology recommendations for screening, follow-up, referral, and treatment based on resource settings. Ophthalmol 125(10):1608–1622. https://doi.org/10.1016/j.ophtha.2018.04.007

    Article  Google Scholar 

  • Wu Y, Xia Y, Song Y, Zhang Y, Cai W (2018) Multiscale network followed network model for retinal vessel segmentation. In: International conference on medical image computing and computer-assisted intervention, pp 119–126. https://doi.org/10.1007/978-3-030-00934-2_14

  • Wu Y, Xia Y, Song Y, Zhang D, Liu D, Zhang C, Cai W (2019) Vessel-net: retinal vessel segmentation under multi-path supervision. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 264–272. https://doi.org/10.1007/978-3-030-32239-7_30

  • Xie S, Tu Z (2017) Holistically-nested edge detection. Int J Comput Vis 125(1–3):3–18. https://doi.org/10.1007/s11263-017-1004-z

    Article  MathSciNet  Google Scholar 

  • Xu R, Ye X, Jiang G, Liu T, Li L, Tanaka S (2020) Retinal vessel segmentation via a semantics and multi-scale aggregation network. IEEE international conference on acoustics. Speech and signal processing (ICASSP), IEEE, pp 1085–1089

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

    Article  Google Scholar 

  • Yan Z, Yang X, Cheng KT (2018b) A three-stage deep learning model for accurate retinal vessel segmentation. IEEE J Biomed Health Inform 23(4):1427–1436. https://doi.org/10.1109/JBHI.2018.2872813

    Article  Google Scholar 

  • Yin Y, Adel M, Bourennane S (2012) Retinal vessel segmentation using a probabilistic tracking method. Pattern Recognit 45(4):1235–1244. https://doi.org/10.1016/j.patcog.2011.09.019

    Article  MATH  Google Scholar 

  • Yu H, Barriga S, Agurto C, Zamora G, Bauman W, Soliz P (2012) Fast vessel segmentation in retinal images using multi-scale enhancement and second-order local entropy. Medical imaging 2012: computer-aided diagnosis. International Society for Optics and Photonics, SPIE, pp 386–397

  • Zhao Y, Zheng Y, Liu Y, Zhao Y, Luo L, Yang S, Na T, Wang Y, Liu J (2017) Automatic 2-d/3-d vessel enhancement in multiple modality images using a weighted symmetry filter. IEEE Trans Med Imaging 37(2):438–450

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an, 710055, China

    Song Guo

Authors
  1. Song Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Song Guo.

Ethics declarations

Funding

This work is supported by PhD research startup foundation of Xi’an University of Architecture and Technology (No.1960320048).

Conflict of interest

The authors declare that they have no conflict of interest.

Availability of data and material

All data generated and used during this study will available at https://github.com/guomugong/DPN.

Code availability

The source code will be available at https://github.com/guomugong/DPN.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work is supported by PhD research startup foundation of Xi’an University of Architecture and Technology (No.1960320048).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, S. DPN: detail-preserving network with high resolution representation for efficient segmentation of retinal vessels. J Ambient Intell Human Comput 14, 5689–5702 (2023). https://doi.org/10.1007/s12652-021-03422-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-021-03422-3

Keywords

Search

Navigation