Automated Segmentation of the Choroid in EDI-OCT Images with Retinal Pathology Using Convolution Neural Networks

  • Min Chen
  • Jiancong Wang
  • Ipek Oguz
  • Brian L. VanderBeek
  • James C. Gee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10554)

Abstract

The choroid plays a critical role in maintaining the portions of the eye responsible for vision. Specific alterations in the choroid have been associated with several disease states, including age-related macular degeneration (AMD), central serous chorioretinopathy, retinitis pigmentosa and diabetes. In addition, choroid thickness measures have been shown as a predictive biomarker for treatment response and visual function. Where several approaches currently exist for segmenting the choroid in optical coherence tomography (OCT) images of healthy retina, very few are capable of addressing images with retinal pathology. The difficulty is due to existing methods relying on first detecting the retinal boundaries before performing the choroidal segmentation. Performance suffers when these boundaries are disrupted or suffer large morphological changes due to disease, and cannot be found accurately. In this work, we show that a learning based approach using convolutional neural networks can allow for the detection and segmentation of the choroid without the prerequisite delineation of the retinal layers. This avoids the need to model and delineate unpredictable pathological changes in the retina due to disease. Experimental validation was performed using 62 manually delineated choroid segmentations of retinal enhanced depth OCT images from patients with AMD. Our results show segmentation accuracy that surpasses those reported by state of the art approaches on healthy retinal images, and overall high values in images with pathology, which are difficult to address by existing methods without pathology specific heuristics.

Keywords

Segmentation Deep learning Convolution neural network Retina EDI-OCT 

References

  1. 1.
    Chung, S.E., Kang, S.W., Lee, J.H., Kim, Y.T.: Choroidal thickness in polypoidal choroidal vasculopathy and exudative age-related macular degeneration. Ophthalmology 118(5), 840–845 (2011)CrossRefGoogle Scholar
  2. 2.
    Dhoot, D.S., Huo, S., Yuan, A., Xu, D., Srivistava, S., Ehlers, J.P., Traboulsi, E., Kaiser, P.K.: Evaluation of choroidal thickness in retinitis pigmentosa using enhanced depth imaging optical coherence tomography. Br. J. Ophthalmol. 97(1), 66–69 (2013)CrossRefGoogle Scholar
  3. 3.
    Esmaeelpour, M., Brunner, S., Ansari-Shahrezaei, S., Nemetz, S., Považay, B., Kajic, V., Drexler, W., Binder, S.: Choroidal thinning in diabetes type 1 detected by 3-Dimensional 1060 nm optical coherence tomography. Investig. Ophthalmol. Visual Sci. 53(11), 6803–6809 (2012)CrossRefGoogle Scholar
  4. 4.
    Kang, H.M., Kwon, H.J., Yi, J.H., Lee, C.S., Lee, S.C.: Subfoveal choroidal thickness as a potential predictor of visual outcome and treatment response after intravitreal ranibizumab injections for typical exudative age-related macular degeneration. Am. J. Ophthalmol. 157(5), 1013–1021 (2014)CrossRefGoogle Scholar
  5. 5.
    Moutray, T., Alarbi, M., Mahon, G., Stevenson, M., Chakravarthy, U.: Relationships between clinical measures of visual function, fluorescein angiographic and optical coherence tomography features in patients with subfoveal choroidal neovascularisation. Br. J. Ophthalmol. 92(3), 361–364 (2008)CrossRefGoogle Scholar
  6. 6.
    Spaide, R.F., Koizumi, H., Pozonni, M.C.: Enhanced depth imaging spectral-domain optical coherence tomography. Am. J. Ophthalmol. 146(4), 496–500 (2008)CrossRefGoogle Scholar
  7. 7.
    Kajić, V., Esmaeelpour, M., Považay, B., Marshall, D., Rosin, P.L., Drexler, W.: Automated choroidal segmentation of 1060 nm OCT in healthy and pathologic eyes using a statistical model. Biomed. Opt. Express 3(1), 86–103 (2012)CrossRefGoogle Scholar
  8. 8.
    Zhang, L., Lee, K., Niemeijer, M., Mullins, R.F., Sonka, M., Abramoff, M.D.: Automated segmentation of the choroid from clinical SD-OCT. Investig. Ophthalmol. Visual Sci. 53(12), 7510–7519 (2012)CrossRefGoogle Scholar
  9. 9.
    Kajić, V., Esmaeelpour, M., Glittenberg, C., Kraus, M.F., Honegger, J., Othara, R., Binder, S., Fujimoto, J.G., Drexler, W.: Automated three-dimensional choroidal vessel segmentation of 3D 1060 nm OCT retinal data. Biomed. Opt. Express 4(1), 134–150 (2013)CrossRefGoogle Scholar
  10. 10.
    Hu, Z., Wu, X., Ouyang, Y., Ouyang, Y., Sadda, S.R.: Semiautomated segmentation of the choroid in spectral-domain optical coherence tomography volume scans. Investig. Ophthalmol. Visual Sci. 54(3), 1722–1729 (2013)CrossRefGoogle Scholar
  11. 11.
    Tian, J., Marziliano, P., Baskaran, M., Tun, T.A., Aung, T.: Automatic segmentation of the choroid in enhanced depth imaging optical coherence tomography images. Biomed. Opt. Express 4(3), 397–411 (2013)CrossRefGoogle Scholar
  12. 12.
    Zhang, L., Buitendijk, G.H., Lee, K., Sonka, M., Springelkamp, H., Hofman, A., Vingerling, J.R., Mullins, R.F., Klaver, C.C., Abràmoff, M.D.: Validity of automated choroidal segmentation in SS-OCT and SD-OCT. Investig. Ophthalmol. Visual Sci. 56(5), 3202–3211 (2015)CrossRefGoogle Scholar
  13. 13.
    Vupparaboina, K.K., Nizampatnam, S., Chhablani, J., Richhariya, A., Jana, S.: Automated estimation of choroidal thickness distribution and volume based on OCT images of posterior visual section. Comput. Med. Imaging Graph. 46, 315–327 (2015)CrossRefGoogle Scholar
  14. 14.
    Philip, A.-M., Gerendas, B.S., Zhang, L., Faatz, H., Podkowinski, D., Bogunovic, H., Abramoff, M.D., Hagmann, M., Leitner, R., Simader, C., et al.: Choroidal thickness maps from spectral domain and swept source optical coherence tomography: algorithmic versus ground truth annotation. Br. J. Ophthalmol. 1–5 (2016)Google Scholar
  15. 15.
    Chen, Q., Fan, W., Niu, S., Shi, J., Shen, H., Yuan, S.: Automated choroid segmentation based on gradual intensity distance in hd-oct images. Opt. Express 23(7), 8974–8994 (2015)CrossRefGoogle Scholar
  16. 16.
    González-López, A., Remeseiro, B., Ortega, M., Penedo, M.G., Charlón, P.: A texture-based method for choroid segmentation in retinal EDI-OCT images. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2015. LNCS, vol. 9520, pp. 487–493. Springer, Cham (2015). doi:10.1007/978-3-319-27340-2_61 CrossRefGoogle Scholar
  17. 17.
    Zhang, L., Sonka, M., Folk, J.C., Russell, S.R., Abramoff, M.D.: Quantifying disrupted outer retinal-subretinal layer in SD-OCT images in choroidal neovascularization. Investig. Ophthalmol. Visual Sci. 55, 2329–2335 (2014)CrossRefGoogle Scholar
  18. 18.
    Avidan, S., Shamir, A.: Seam carving for content-aware image resizing. ACM Trans. Graph. (TOG) 26(3), 10 (2007). ACMCrossRefGoogle Scholar
  19. 19.
    Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. arXiv preprint arXiv:1511.00561 (2015)
  20. 20.
    Yushkevich, P.A., Piven, J., Hazlett, H.C., Smith, R.G., Ho, S., Gee, J.C., Gerig, G.: User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3), 1116–1128 (2006)CrossRefGoogle Scholar
  21. 21.
    Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)CrossRefGoogle Scholar
  22. 22.
    Garvin, M.K., Abramoff, M.D., Wu, X., Russell, S.R., Burns, T.L., Sonka, M.: Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images. IEEE Trans. Med. Imaging 28(9), 1436–1447 (2009)CrossRefGoogle Scholar
  23. 23.
    Lang, A., Carass, A., Hauser, M., Sotirchos, E.S., Calabresi, P.A., Ying, H.S., Prince, J.L.: Retinal layer segmentation of macular OCT images using boundary classification. Biomed. Opt. Express 4(7), 1133–1152 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Min Chen
    • 1
  • Jiancong Wang
    • 1
  • Ipek Oguz
    • 1
  • Brian L. VanderBeek
    • 2
  • James C. Gee
    • 1
  1. 1.Department of RadiologyUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.Department of OphthalmologyUniversity of PennsylvaniaPhiladelphiaUSA

Personalised recommendations