Advertisement

The Visual Computer

, Volume 34, Issue 9, pp 1209–1224 | Cite as

Visual analysis of retinal changes with optical coherence tomography

  • Martin Röhlig
  • Christoph Schmidt
  • Ruby Kala Prakasam
  • Paul Rosenthal
  • Heidrun Schumann
  • Oliver Stachs
Original Article

Abstract

Optical coherence tomography (OCT) enables noninvasive high-resolution 3D imaging of the human retina, and thus plays a fundamental role in detecting a wide range of ocular diseases. Despite the diagnostic value of OCT, managing and analyzing resulting data is challenging. We apply two visual analysis strategies for supporting retinal assessment in practice. First, we provide an interface for unifying and structuring data from different sources into a common basis. Fusing that basis with medical records and augmenting it with analytically derived information facilitates thorough investigations. Second, we present a tailored visual analysis tool for presenting, emphasizing, selecting, and comparing different aspects of the attributed data. This enables free exploration, reducing the data to relevant subsets, and focusing on details. By applying both strategies, we effectively enhance the management and the analysis of retinal OCT data for assisting medical diagnoses. Domain experts applied our solution successfully to study early retinal changes in patients suffering from type 1 diabetes mellitus.

Keywords

Visual analysis Optical coherence tomography OCT data Ophthalmology Retina 

Notes

Acknowledgements

The authors wish to thank Heidelberg Engineering GmbH for providing OCT hardware, and respective software interfaces and analysis software.

References

  1. 1.
    Aaker, G.D., Gracia, L., Myung, J.S., Borcherding, V., Banfelder, J.R., D’Amico, D.J., Kiss, S.: Three-dimensional reconstruction and analysis of vitreomacular traction: quantification of cyst volume and vitreoretinal interface area. Arch. Ophthalmol. 129(6), 805–820 (2011).  https://doi.org/10.1001/archophthalmol.2011.123 CrossRefGoogle Scholar
  2. 2.
    Arias-Hernandez, R., Kaastra, L.T., Green, T.M., Fisher, B.: Pair analytics: capturing reasoning processes in collaborative visual analytics. In: Proceedings of the Hawaii International Conference on System Sciences, pp. 1–10. IEEE Computer Society, Washington, DC, USA (2011).  https://doi.org/10.1109/HICSS.2011.339
  3. 3.
    Baghaie, A., Yu, Z., D’Souza, R.M.: State-of-the-art in retinal optical coherence tomography image analysis. Quant. Imaging Med. Surg. 5(4), 603–617 (2015).  https://doi.org/10.3978/j.issn.2223-4292.2015.07.02 Google Scholar
  4. 4.
    Barla, P., Thollot, J., Markosian, L.: X-toon: an extended toon shader. In: DeCarlo, D., Markosian, L. (eds.) Proceedings of the International Symposium on Non-photorealistic Animation and Rendering, pp. 127–132. ACM, Annecy, France (2006).  https://doi.org/10.1145/1124728.1124749
  5. 5.
    Berufsverband der Augenärzte Deutschlands e. V., Deutsche Ophthalmologische Gesellschaft, Retinologische Gesellschaft e. V.: Quality assurance of optical coherence tomography for diagnostics of the fundus: positional statement of the BVA, DOG and RG. Der Ophthalmologe 114(7), 617–624 (2017).  https://doi.org/10.1007/s00347-017-0508-9
  6. 6.
    Chen, Q., Huang, S., Ma, Q., Lin, H., Pan, M., Liu, X., Lu, F., Shen, M.: Ultra-high resolution profiles of macular intra-retinal layer thicknesses and associations with visual field defects in primary open angle glaucoma. Sci. Rep. 7, 41100 (2017).  https://doi.org/10.1038/srep41100 CrossRefGoogle Scholar
  7. 7.
    Chen, Y., Li, J., Yan, Y., Shen, X.: Diabetic macular morphology changes may occur in the early stage of diabetes. BMC Ophthalmol. 16, 12 (2016).  https://doi.org/10.1186/s12886-016-0186-4 CrossRefGoogle Scholar
  8. 8.
    De Clerck, E.E., Schouten, J.S., Berendschot, T.T., Kessels, A.G., Nuijts, R.M., Beckers, H.J., Schram, M.T., Stehouwer, C.D., Webers, C.A.: New ophthalmologic imaging techniques for detection and monitoring of neurodegenerative changes in diabetes: a systematic review. Lancet Diabetes Endocrinol. 3(8), 653–663 (2015).  https://doi.org/10.1016/S2213-8587(15)00136-9 CrossRefGoogle Scholar
  9. 9.
    Drexler, W., Morgner, U., Ghanta, R.K., Kärtner, F.X., Schuman, J.S., Fujimoto, J.G.: Ultrahigh-resolution ophthalmic optical coherence tomography. Nat. Med. 7(4), 502–507 (2001).  https://doi.org/10.1038/86589 CrossRefGoogle Scholar
  10. 10.
    Duncan, M.D., Bashkansky, M., Reintjes, J.: Subsurface defect detection in materials using optical coherence tomography. Opt. Express 2(13), 540–545 (1998).  https://doi.org/10.1364/OE.2.000540 CrossRefGoogle Scholar
  11. 11.
    Early Treatment Diabetic Retinopathy Study Research: Grading diabetic retinopathy from stereoscopic color fundus photographs—an extension of the modified airlie house classification: ETDRS report number 10. Ophthalmology 98(5), 786–806 (1991).  https://doi.org/10.1016/S0161-6420(13)38012-9 CrossRefGoogle Scholar
  12. 12.
    Ehnes, A., Wenner, Y., Friedburg, C., Preising, M.N., Bowl, W., Sekundo, W., zu Bexten, E.M., Stieger, K., Lorenz, B.: Optical coherence tomography (OCT) device independent intraretinal layer segmentation. Transl. Vis. Sci. Technol. 3, 1 (2014).  https://doi.org/10.1167/tvst.3.1.1 CrossRefGoogle Scholar
  13. 13.
    El-Fayoumi, D., Badr Eldine, N.M., Esmael, A.F., Ghalwash, D., Soliman, H.M.: Retinal nerve fiber layer and ganglion cell complex thicknesses are reduced in children with type 1 diabetes with no evidence of vascular retinopathy. Investig. Ophthalmol. Vis. Sci. 57(13), 5355 (2016).  https://doi.org/10.1167/iovs.16-19988 CrossRefGoogle Scholar
  14. 14.
    Elmqvist, N., Vande Moere, A., Jetter, H.C., Cernea, D., Reiterer, H., Jankun-Kelly, T.J.: Fluid interaction for information visualization. Inf. Vis. 10(4), 327–340 (2011).  https://doi.org/10.1177/1473871611413180 CrossRefGoogle Scholar
  15. 15.
    Garrido, M.G., Beck, S.C., Mühlfriedel, R., Julien, S., Schraermeyer, U., Seeliger, M.W.: Towards a quantitative OCT image analysis. PLoS ONE 9(6), 1–10 (2014).  https://doi.org/10.1371/journal.pone.0100080 Google Scholar
  16. 16.
    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).  https://doi.org/10.1109/TMI.2009.2016958 CrossRefGoogle Scholar
  17. 17.
    Glaßer, S., Hoffmann, T., Boese, A., Voß, S., Kalinski, T., Skalej, M., Preim, B.: Virtual inflation of the cerebral artery wall for the integrated exploration of OCT and histology data. Comput. Graph. Forum (2016).  https://doi.org/10.1111/cgf.12994 Google Scholar
  18. 18.
    Gleicher, M., Albers, D., Walker, R., Jusufi, I., Hansen, C.D., Roberts, J.C.: Visual comparison for information visualization. Inf. Vis. 10(4), 289–309 (2011).  https://doi.org/10.1177/1473871611416549 CrossRefGoogle Scholar
  19. 19.
    Glittenberg, C., Krebs, I., Falkner-Radler, C., Zeiler, F., Haas, P., Hagen, S., Binder, S.: Advantages of using a ray-traced, three-dimensional rendering system for spectral domain cirrus HD-OCT to visualize subtle structures of the vitreoretinal interface. Ophthalmic Surg. Lasers Imaging 40(2), 127–134 (2009).  https://doi.org/10.3928/15428877-20090301-08 CrossRefGoogle Scholar
  20. 20.
    Hall, K.W., Perin, C., Kusalik, P.G., Gutwin, C., Carpendale, S.: Formalizing emphasis in information visualization. Comput. Graph. Forum 35(3), 717–737 (2016).  https://doi.org/10.1111/cgf.12936 CrossRefGoogle Scholar
  21. 21.
    Harrower, M., Brewer, C.A.: Colorbrewer.org: an online tool for selecting colour schemes for maps. Cartogr. J. 40(1), 27–37 (2003).  https://doi.org/10.1179/000870403235002042 CrossRefGoogle Scholar
  22. 22.
    Kahl, S., Ritter, M., Rosenthal, P.: Automated assessment of the injury situation in patients with age-related macular degeneration (AMD). In: Leon, F.P., Heizmann, M. (eds.) Proceedings of Forum Bildverarbeitung, pp. 179–190. KIT Scientific Publishing, Regensburg, Germany (2014).  https://doi.org/10.5445/KSP/1000043608
  23. 23.
    Keim, D.A., Mansmann, F., Schneidewind, J., Thomas, J., Ziegler, H.: Visual Analytics: Scope and Challenges, pp. 76–90. Springer, Berlin (2008).  https://doi.org/10.1007/978-3-540-71080-6_6 Google Scholar
  24. 24.
    Koleva-Georgieva, D.N.: Optical coherence tomography—segmentation performance and retinal thickness measurement errors. Eur. Ophthalmic Rev 6(2), 78–82 (2012).  https://doi.org/10.17925/EOR.2012.06.02.78 CrossRefGoogle Scholar
  25. 25.
    Kosara, R., Miksch, S., Hauser, H.: Semantic depth of field. In: Proceedings of the IEEE Symposium on Information Visualization, pp. 97–104. IEEE Computer Society, San Diego, CA, USA (2001).  https://doi.org/10.1109/INFVIS.2001.963286
  26. 26.
    Mayer, M.A., Hornegger, J., Mardin, C.Y., Tornow, R.P.: Retinal nerve fiber layer segmentation on FD-OCT scans of normal subjects and glaucoma patients. Biomed. Opt. Exp. 1(5), 1358–1383 (2010).  https://doi.org/10.1364/BOE.1.001358 CrossRefGoogle Scholar
  27. 27.
    Moisseiev, E., Park, S., Yiu, G., Werner, J.S., Zawadzki, R.J.: The third dimension: advantages of 3D-OCT in retina—unprecedented detail of perfusion and other structures. Retin. Physician 13, 24–33 (2016)Google Scholar
  28. 28.
    Oat, C., Sander, P.V.: Ambient aperture lighting. In: Proceedings of the Symposium on Interactive 3D Graphics and Games, pp. 61–64. ACM, Seattle, WA, USA (2007).  https://doi.org/10.1145/1230100.1230111
  29. 29.
    Odell, D., Dubis, A.M., Lever, J.F., Stepien, K.E., Carroll, J.: Assessing errors inherent in OCT-derived macular thickness maps. J. Ophthalmol. (2011).  https://doi.org/10.1155/2011/692574
  30. 30.
    Placet, V., Méteau, J., Froehly, L., Salut, R., Boubakar, M.L.: Investigation of the internal structure of hemp fibres using optical coherence tomography and focused ion beam transverse cutting. J. Mater. Sci. 49(24), 8317–8327 (2014).  https://doi.org/10.1007/s10853-014-8540-5 CrossRefGoogle Scholar
  31. 31.
    Probst, J., Koch, P., Hüttmann, G.: Real-time 3D rendering of optical coherence tomography volumetric data. In: Andersen, P.E., Bouma, B.E. (eds.) Proceedings of SPIE optical coherence tomography and coherence techniques IV, pp. 73720Q–73731Q. SPIE, Munich, Germany (2009).  https://doi.org/10.1117/12.831785
  32. 32.
    Röhlig, M., Jünemann, A., Fischer, D.C., Prakasam, R.K., Stachs, O., Schumann, H.: Visual analysis of retinal OCT data. Klinische Monatsblätter für Augenheilkunde 234(12), 1463–1471 (2017).  https://doi.org/10.1055/s-0043-121705 CrossRefGoogle Scholar
  33. 33.
    Röhlig, M., Rosenthal, P., Schmidt, C., Schumann, H., Stachs, O.: Visual analysis of optical coherence tomography data in ophthalmology. In: Sedlmair, M., Tominski, C. (eds.) Proceedings of the EuroVis Workshop on Visual Analytics (EuroVA). The Eurographics Association, Barcelona, Spain (2017).  https://doi.org/10.2312/eurova.20171117
  34. 34.
    Rosenthal, P., Ritter, M., Kowerko, D., Heine, C.: OphthalVis—making data analytics of optical coherence tomography reproducible. In: Lawonn, K., Hlawitschka, M., Rosenthal, P. (eds.) Proceedings of EuroVis Workshop on Reproducibility, Verification, and Validation in Visualization. The Eurographics Association, Groningen, Netherlands (2016).  https://doi.org/10.2312/eurorv3.20161109
  35. 35.
    Rosenthal, P., Ritter, M., Kowerko, D., Heine, C.: Unified OCT explorer (2016). http://bitbucket.org/uocte/
  36. 36.
    Schindelin, J., Rueden, C.T., Hiner, M.C., Eliceiri, K.W.: The ImageJ ecosystem: an open platform for biomedical image analysis. Mol. Reprod. Dev. 82(7–8), 518–529 (2015).  https://doi.org/10.1002/mrd.22489 CrossRefGoogle Scholar
  37. 37.
    Schulze, J.P., Schulze-Döbold, C., Erginay, A., Tadayoni, R.: Visualization of three-dimensional ultra-high resolution OCT in virtual reality. Stud. Health Technol. Inform. 184, 387–391 (2013).  https://doi.org/10.3233/978-1-61499-209-7-387 Google Scholar
  38. 38.
    Sylwestrzak, M., Szlag, D., Szkulmowski, M., Targowski, P.: Real-time massively parallel processing of spectral optical coherence tomography data on graphics processing units. In: Leitgeb, R.A., Bouma, B.E. (eds.) Proceedings of SPIE Optical Coherence Tomography and Coherence Techniques V, pp. 80910V–80917V. Munich, Germany (2011).  https://doi.org/10.1117/12.889805
  39. 39.
    van Dijk, H.W., Kok, P.H.B., Garvin, M., Sonka, M., DeVries, J.H., Michels, R.P.J., van Velthoven, M.E.J., Schlingemann, R.O., Verbraak, F.D., Abràmoff, M.D.: Selective loss of inner retinal layer thickness in type 1 diabetic patients with minimal diabetic retinopathy. Investig. Ophthalmol. Vis. Sci. 50(7), 3404 (2009).  https://doi.org/10.1167/iovs.08-3143 CrossRefGoogle Scholar
  40. 40.
    Wojtkowski, M., Srinivasan, V., Fujimoto, J.G., Ko, T., Schuman, J.S., Kowalczyk, A., Duker, J.S.: Three-dimensional retinal imaging with high-speed ultrahigh-resolution optical coherence tomography. Ophthalmology 112(10), 1734–1746 (2005).  https://doi.org/10.1016/j.ophtha.2005.05.023 CrossRefGoogle Scholar
  41. 41.
    Yoshimura, N., Hangai, M.: OCT Atlas. Springer, Germany (2014)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Martin Röhlig
    • 1
  • Christoph Schmidt
    • 1
  • Ruby Kala Prakasam
    • 2
  • Paul Rosenthal
    • 1
  • Heidrun Schumann
    • 1
  • Oliver Stachs
    • 2
  1. 1.Institute for Computer ScienceUniversity of RostockRostockGermany
  2. 2.Department of OphthalmologyUniversity of RostockRostockGermany

Personalised recommendations