[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Joint Diabetic Macular Edema Segmentation and Characterization in OCT Images

  • Original Paper
  • Published:
Journal of Digital Imaging Aims and scope Submit manuscript

Abstract

The automatic identification and segmentation of edemas associated with diabetic macular edema (DME) constitutes a crucial ophthalmological issue as they provide useful information for the evaluation of the disease severity. According to clinical knowledge, the DME disorder can be categorized into three main pathological types: serous retinal detachment (SRD), cystoid macular edema (CME), and diffuse retinal thickening (DRT). The implementation of computational systems for their automatic extraction and characterization may help the clinicians in their daily clinical practice, adjusting the diagnosis and therapies and consequently the life quality of the patients. In this context, this paper proposes a fully automatic system for the identification, segmentation and characterization of the three ME types using optical coherence tomography (OCT) images. In the case of SRD and CME edemas, different approaches were implemented adapting graph cuts and active contours for their identification and precise delimitation. In the case of the DRT edemas, given their fuzzy regional appearance that requires a complex extraction process, an exhaustive analysis using a learning strategy was designed, exploiting intensity, texture, and clinical-based information. The different steps of this methodology were validated with a heterogeneous set of 262 OCT images, using the manual labeling provided by an expert clinician. In general terms, the system provided satisfactory results, reaching Dice coefficient scores of 0.8768, 0.7475, and 0.8913 for the segmentation of SRD, CME, and DRT edemas, respectively.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Alemán-Flores M, Álvarez L, Caselles V: Texture-oriented anisotropic filtering and geodesic active contours in breast tumor ultrasound segmentation. J Math Imaging Vis 28(1):81–97, 2007

    Article  Google Scholar 

  2. Baamonde S, de Moura J, Novo J, Ortega M: Automatic detection of epiretinal membrane in OCT images by means of local luminosity patterns. In: International Work-Conference on Artificial Neural Networks, 2017, pp 222–235

  3. Bi J, Bennett K, Embrechts M, Breneman C, Song M: Dimensionality reduction via sparse support vector machines. J Mach Learn Res 3(Mar):1229–1243, 2003

    Google Scholar 

  4. Blinder KJ, Dugel PU, Chen S, Jumper JM, Walt JG, Hollander DA, Scott LC: Anti-VEGF treatment of Diabetic Macular Edema in clinical practice: effectiveness and patterns of use (ECHO Study Report 1). Clin Ophthalmol (Auckland, NZ) 11:393, 2017

    Article  CAS  Google Scholar 

  5. Bourne RR, Stevens GA, White RA, Smith JL, Flaxman SR, Price H, Jonas JB, Keeffe J, Leasher J, Naidoo K, et al.: Causes of vision loss worldwide, 1990–2010: a systematic analysis. Lancet Glob Health 1(6):e339–e349, 2013

    Article  PubMed  Google Scholar 

  6. Caselles V, Kimmel R, Sapiro G: Geodesic active contours. Int J Comput Vis 22(1):61–79, 1997

    Article  Google Scholar 

  7. Chan TF, Vese LA: Active contours without edges. IEEE Trans Image Process 10(2):266–277, 2001

    Article  CAS  PubMed  Google Scholar 

  8. Chen X, Udupa JK, Bagci U, Zhuge Y, Yao J: Medical image segmentation by combining graph cuts and oriented active appearance models. IEEE Trans Image Process 21(4):2035–2046, 2012

    Article  PubMed  PubMed Central  Google Scholar 

  9. Chen Y, Wang Z, Zhao W: Liver segmentation in CT images using Chan-Vese model. In: 2009 First International Conference on Information Science and Engineering. IEEE, 2009, pp 3669–3672

  10. Das R, Spence G, Hogg RE, Stevenson M, Chakravarthy U: Disorganization of inner retina and outer retinal morphology in diabetic macular edema. JAMA Ophthalmol 136(2):202–208, 2018

    Article  PubMed  PubMed Central  Google Scholar 

  11. Ding W, Young M, Bourgault S, Lee S, Albiani DA, Kirker AW, Forooghian F, Sarunic M, Merkur AB, Beg MF: Automatic detection of subretinal fluid and sub-retinal pigment epithelium fluid in Optical Coherence Tomography images. In: 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, 2013, pp 7388–7391

  12. Friedman N, Geiger D, Goldszmidt M: Bayesian network classifiers. Mach Learn 29(2-3):131–163, 1997

    Article  Google Scholar 

  13. Funka-Lea G, Boykov Y, Florin C, Jolly MP, Moreau-Gobard R, Ramaraj R, Rinck D: Automatic heart isolation for CT coronary visualization using graph-cuts. In: 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006. IEEE, 2006, pp 614–617

  14. Girish G, Thakur B, Chowdhury SR, Kothari AR, Rajan J: Segmentation of intra-retinal cysts from Optical Coherence Tomography images using a fully convolutional neural network model. IEEE J Biomed Health Inform 23(1):296–304, 2019

    Article  CAS  PubMed  Google Scholar 

  15. González-López A., de Moura J, Novo J, Ortega M, Penedo M: Robust segmentation of retinal layers in optical coherence tomography images based on a multistage active contour model. Heliyon 5(2):1–34, 2019

    Article  Google Scholar 

  16. Hernandez M, Frangi AF: Non-parametric geodesic active regions: method and evaluation for cerebral aneurysms segmentation in 3DRA and CTA. Med Image Anal 11(3):224–241, 2007

    Article  PubMed  Google Scholar 

  17. Keerthi SS, Shevade SK, Bhattacharyya C, Murthy KRK: Improvements to platt’s SMO algorithm for SVM classifier design. Neural Comput 13(3):637–649, 2001

    Article  Google Scholar 

  18. Kroon DJ, Slump CH, Maal TJ: Optimized anisotropic rotational invariant diffusion scheme on cone-beam CT. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 2010, pp 221–228

  19. Lee CS, Tyring AJ, Deruyter NP, Wu Y, Rokem A, Lee AY: Deep-learning based, automated segmentation of Macular Edema in Optical Coherence Tomography. Biomed Opt Express 8(7):3440–3448, 2017

    Article  PubMed  PubMed Central  Google Scholar 

  20. Lee H, Kang KE, Chung H, Kim HC: Automated segmentation of lesions including subretinal hyperreflective material in neovascular age-related macular degeneration. Am J Ophthalmol 191:64–75, 2018

    Article  PubMed  Google Scholar 

  21. Lissack T, Fu KS: Error estimation in pattern recognition via lα-distance between posterior density functions. IEEE Trans Inform Theory 22(1):34–45, 1976

    Article  Google Scholar 

  22. Liu C, Wechsler H: Robust coding schemes for indexing and retrieval from large face databases. IEEE Trans Image Process 9(1):132–137, 2000

    Article  CAS  PubMed  Google Scholar 

  23. Malladi R, Sethian JA, Vemuri BC: Shape modeling with front propagation: a level set approach. IEEE Trans Pattern Anal Mach Intell 17(2):158–175, 1995

    Article  Google Scholar 

  24. Marmor MF: Mechanisms of fluid accumulation in retinal edema. In: Macular Edema. Springer, 2000, pp 35–45

  25. Montuoro A, Waldstein S, Gerendas B, Schmidt-Erfurth U, Bogunović H.: Joint retinal layer and fluid segmentation in OCT scans of eyes with severe macular edema using unsupervised representation and auto-context. Biomed Opt Express 8(3):1874–1888, 2017

    Article  PubMed  PubMed Central  Google Scholar 

  26. Mumford D, Shah J: Optimal approximations by piecewise smooth functions and associated variational problems. Commun Pure Appl Math 42(5):577–685, 1989

    Article  Google Scholar 

  27. Nie F, Huang H, Cai X, Ding C: Efficient and robust feature selection via joint ℓ2, 1-norms minimization. In: Advances in Neural Information Processing Systems, 2010, pp 1813–1821

  28. Novosel J, Vermeer KA, de Jong JH, Wang Z, van Vliet LJ: Joint segmentation of retinal layers and focal lesions in 3-D OCT data of topologically disrupted retinas. IEEE Trans Med Imaging 36(6):1276–1286, 2017

    Article  PubMed  Google Scholar 

  29. Otani T, Kishi S, Maruyama Y: Patterns of diabetic macular edema with optical coherence tomography. Am J Ophthalmol 127(6):688–693, 1999

    Article  CAS  PubMed  Google Scholar 

  30. Panozzo G, Parolini B, Gusson E, Mercanti A, Pinackatt S, Bertoldo G, Pignatto S: Diabetic macular edema: an OCT-based classification. In: Seminars in Ophthalmology, vol 19, 2004, pp 13–20

  31. Quinlan JR: Improved use of continuous attributes in c4.5. J Artif Intell Res 4:77–90, 1996

    Article  Google Scholar 

  32. Rashno A, Koozekanani DD, Drayna PM, Nazari B, Sadri S, Rabbani H, Parhi KK: Fully automated segmentation of fluid/cyst regions in Optical Coherence Tomography images with diabetic macular edema Using neutrosophic sets and graph algorithms. IEEE Trans Biomed Eng 65(5):989–1001, 2018

    PubMed  Google Scholar 

  33. Rother C, Kolmogorov V, Blake A: Grabcut: Interactive foreground extraction using iterated graph cuts. In: ACM Transactions on graphics (TOG), vol 23. ACM, 2004, pp 309–314

  34. Roy A, Conjeti S, Phani Karri S, Sheet D, Katouzian A, Wachinger C, Navab N: Relaynet: retinal layer and fluid segmentation of macular Optical Coherence Tomography using fully convolutional network. Biomed Optics Express 8(8):3627–3642, 2017

    Article  Google Scholar 

  35. Samagaio G, Estévez A., de Moura J, Novo J, Fernandez MI, Ortega M: Automatic macular edema identification and characterization using OCT images. Comput Methods Programs Biomed 163: 47–63, 2018

    Article  PubMed  Google Scholar 

  36. Samagaio G, de Moura J, Novo J, Ortega M: Optical coherence tomography denoising by means of a fourier butterworth Filter-Based approach. In: International Conference on Image Analysis and Processing. Springer, 2017, pp 422–432

  37. Samagaio G, de Moura J, Novo J, Ortega M: Optical coherence tomography denoising by means of a fourier butterworth Filter-Based approach. In: International Conference on Image Analysis and Processing. Springer, 2017, pp 422–432

  38. Samagaio G, de Moura J, Novo J, Ortega M: Automatic segmentation of diffuse retinal thickening edemas using Optical Coherence Tomography images. Procedia Comput Sci 126:472–481, 2018

    Article  Google Scholar 

  39. Schlegl T, Waldstein S, Bogunovic H, Endstraßer F, Sadeghipour A, Philip AM, Podkowinski D, Gerendas BS, Langs G, Schmidt-Erfurth U: Fully automated detection and quantification of macular fluid in OCT using deep learning. Ophthalmology 125(4):549–558, 2018

    Article  PubMed  Google Scholar 

  40. Sidibé D, Sankar S, Lemaître G, Rastgoo M, Massich J, Cheung C, Tan G, Milea D, et al.: An anomaly detection approach for the identification of DME patients using spectral domain Optical Coherence Tomography Images. Computer Methods and Programs in Biomedicine 139:109–117, 2017

    Article  PubMed  Google Scholar 

  41. Siedlecki W, Sklansky J: On automatic feature selection. Int J Pattern Recognit Artif Intell 2(02):197–220, 1988

    Article  Google Scholar 

  42. Srivastava S, Gupta MR, Frigyik BA: Bayesian quadratic discriminant analysis. J Mach Learn Res 8(Jun):1277–1305, 2007

    Google Scholar 

  43. Sun Z, Chen H, Shi F, Wang L, Zhu W, Xiang D, Yan C, Li L, Chen X: An automated framework for 3D serous pigment epithelium detachment segmentation in SD-OCT images. Sci Rep 6(21):739, 2016

    Google Scholar 

  44. Venhuizen FG, van Ginneken B, Liefers B, van Asten F, Schreur V, Fauser S, Hoyng C, Theelen T: Sánchez, C.I.: Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor Optical Coherence Tomography. Biomed Opt Express 9(4):1545–1569, 2018

    Article  PubMed  PubMed Central  Google Scholar 

  45. Yang Y: Expert network: Effective and efficient learning from human decisions in text categorization and retrieval. In: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Springer-Verlag New York Inc, 1994, pp 13–22

  46. Yazdanpanah A, Hamarneh G, Smith B, Sarunic M: Intra-retinal layer segmentation in Optical Coherence Tomography using an active contour approach. In: International conference on medical image computing and computer-assisted intervention. Springer, 2009, pp 649–656

  47. Zheng Y, Sahni J, Campa C, Stangos AN, Raj A: Harding, S.P.: Computerized assessment of intraretinal and subretinal fluid regions in spectral-domain Optical Coherence Tomography images of the retina. Am J Ophthalmol 155(2):277–286, 2013

    Article  PubMed  Google Scholar 

  48. Zhu S, Yuille A: Region competition: Unifying Snakes, Region Growing, and bayes/MDL for Multiband Image Segmentation. IEEE Trans Pattern Anal Mach Intell 18(9):884–900, 1996

    Article  Google Scholar 

Download references

Funding

This work is supported by the Instituto de Salud Carlos III, Government of Spain, and FEDER funds through the DTS18/00136 research project and by Ministerio de Ciencia, Innovación y Universidades, Government of Spain through the DPI2015-69948-R and RTI2018-095894-B-I00 research projects. Also, this work has received financial support from the European Union (European Regional Development Fund - ERDF) and the Xunta de Galicia, Centro de Investigación del Sistema Universitário de Galicia, Ref. ED431G 2019/01; and Grupos de Referencia Competitiva, Ref. ED431C 2016-047.

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Technology, University of A Coruña, 15071, A Coruña, Spain

    Joaquim de Moura, Jorge Novo & Marcos Ortega

  2. CITIC - Research Center of Information and Communication Technologies, University of A Coruña, 15071, A Coruña, Spain

    Joaquim de Moura, Jorge Novo & Marcos Ortega

  3. Faculty of Engineering, University of Porto, 4200-465, Porto, Portugal

    Gabriela Samagaio

  4. Department of Ophthalmology, Complejo Hospitalario Universitario de Santiago, 15706, Santiago de Compostela, Spain

    Pablo Almuina & María Isabel Fernández

  5. Instituto Oftalmológico Gómez-Ulla, 15706, Santiago de Compostela, Spain

    María Isabel Fernández

  6. University of Santiago de Compostela, 15705, Santiago de Compostela, Spain

    María Isabel Fernández

Authors
  1. Joaquim de Moura
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gabriela Samagaio
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jorge Novo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pablo Almuina
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. María Isabel Fernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Marcos Ortega
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Joaquim de Moura.

Additional information

Publisher’s Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

de Moura, J., Samagaio, G., Novo, J. et al. Joint Diabetic Macular Edema Segmentation and Characterization in OCT Images. J Digit Imaging 33, 1335–1351 (2020). https://doi.org/10.1007/s10278-020-00360-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10278-020-00360-y

Keywords

Search

Navigation