Automated staging of age-related macular degeneration using optical coherence tomography

Investigative Ophthalmology and Visual Science

Volumn 58, Issue 4, 2017, Pages 2318-2328

  Venhuizen, Freerk G ^a   van Ginneken, Bram ^a   Van Asten, Freekje ^a   van Grinsven, Mark J J P ^a   Fauser, Sascha ^b,c   Hoyng, Carel B ^a   Theelen, Thomas ^a   Sánchez, Clara I ^a  

^a RADBOUD UNIVERSITY MEDICAL CENTER   (Netherlands)

^b UNIVERSITY HOSPITAL OF COLOGNE   (Germany)

^c F HOFFMANN LA ROCHE LTD   (Switzerland)

Author keywords

AMD classification; Automated grading; Machine learning; OCT; Retinal image analysis

Indexed keywords

AGE; ALGORITHM; ARTICLE; DATA ANALYSIS; DISEASE SEVERITY; HUMAN; MACHINE LEARNING; MACULAR DEGENERATION; MAJOR CLINICAL STUDY; OPTICAL COHERENCE TOMOGRAPHY; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; AGED; CLINICAL TRIAL; FEMALE; MALE; MIDDLE AGED; MULTICENTER STUDY; PATHOLOGY; PROCEDURES; REPRODUCIBILITY; RETINA MACULA LUTEA; SEVERITY OF ILLNESS INDEX; VERY ELDERLY;

AGED; AGED, 80 AND OVER; FEMALE; HUMANS; MACULA LUTEA; MACULAR DEGENERATION; MALE; MIDDLE AGED; REPRODUCIBILITY OF RESULTS; ROC CURVE; SEVERITY OF ILLNESS INDEX; TOMOGRAPHY, OPTICAL COHERENCE;

EID: 85018351346     PISSN: 01460404     EISSN: 15525783     Source Type: Journal    
DOI: 10.1167/iovs.16-20541     Document Type: Article

References (44)

1. Bressler NM. Age-related macular degeneration is the leading cause of blindness. JAMA. 2004;291:1900-1901.
2. Klaver CC, Wolfs RC, Assink JJ, van Duijn CM, Hofman A, de Jong PT. Genetic risk of age-related maculopathy: populationbased familial aggregation study. Arch Ophthalmol. 1998;116: 1646-1651.
3. Rosenfeld PJ, Brown DM, Heier JS, et al. Ranibizumab for neovascular age-related macular degeneration. N Engl J Med. 2006;355:1419-1431.
4. Martin DF, Maguire MG, Ying GS, Grunwald JE, Fine SL, Jaffe GJ. Ranibizumab and bevacizumab for neovascular age-related macular degeneration. N Engl J Med. 2011;364:1897-1908.
5. Enders P, Scholz P, Muether PS, Fauser S. Variability of disease activity in patients treated with ranibizumab for neovascular age-related macular degeneration. Eye (Lond). 2016;30:1072-1076.
6. Schmidt-Erfurth U, Waldstein SM. A paradigm shift in imaging biomarkers in neovascular age-related macular degeneration. Prog Retin Eye Res. 2016;50:1-24.
7. Kanagasingam Y, Bhuiyan A, Abramoff MD, Smith RT, Goldschmidt L, Wong TY. Progress on retinal image analysis for age related macular degeneration. Prog Retin Eye Res. 2014;38: 20-42.
8. Adhi M, Duker JS. Optical coherence tomography-current and future applications. Curr Opin Ophthalmol. 2013;24: 213-221.
9. Yehoshua Z, Rosenfeld PJ, Gregori G, Penha F. Spectral domain optical coherence tomography imaging of dry agerelated macular degeneration. Ophthalmic Surg Lasers Imaging. 2010;41(suppl):S6-S14.
10. van de Ven JP, Boon CJ, Smailhodzic D, et al. Short-term changes of Basal laminar drusen on spectral-domain optical coherence tomography. Am J Ophthalmol. 2012;154:560-567.
11. Jain N, Farsiu S, Khanifar AA, et al. Quantitative comparison of drusen segmented on SD-OCT versus drusen delineated on color fundus photographs. Invest Ophthalmol Vis Sci. 2010; 51:4875-4883.
12. Mokwa NF, Ristau T, Keane PA, Kirchhof B, Sadda SR, Liakopoulos S. Grading of age-related macular degeneration: comparison between color fundus photography, fluorescein angiography, and spectral domain optical coherence tomography. J Ophthalmol. 2013;2013:385915.
13. Schuman SG, Koreishi AF, Farsiu S, Jung SH, Izatt JA, Toth CA. Photoreceptor layer thinning over drusen in eyes with agerelated macular degeneration imaged in vivo with spectraldomain optical coherence tomography. Ophthalmology. 2009;116:488-496.e2.
14. De Fauw J, Keane P, Tomasev N, et al. Automated analysis of retinal imaging using machine learning techniques for computer vision. F1000Res. 2016;5:1573.
15. Abramoff MD, Garvin MK, Sonka M. Retinal imaging and image analysis. IEEE Trans Med Imaging. 2010;3:169-208.
16. Kafieh R, Rabbani H, Kermani S. A review of algorithms for segmentation of optical coherence tomography from retina. J Med Signals Sens. 2013;3:45-60.
17. Quellec G, Lee K, Dolejsi M, Garvin MK, Abramoff MD, Sonka M. Three-dimensional analysis of retinal layer texture: identification of fluid-filled regions in SD-OCT of the macula. IEEE Trans Med Imaging. 2010;29:1321-1330.
18. Hu Z, Niemeijer M, Abramoff MD, Garvin MK. Multimodal retinal vessel segmentation from spectral-domain optical coherence tomography and fundus photography. IEEE Trans Med Imaging. 2012;31:1900-1911.
19. Esmaeili M, Dehnavi AM, Rabbani H, Hajizadeh F. Threedimensional segmentation of retinal cysts from spectraldomain optical coherence tomography images by the use of three-dimensional curvelet based K-SVD. J Med Signals Sens. 2016;6:166-171.
20. Schlegl T, Waldstein SM, Vogl WD, Schmidt-Erfurth U, Langs G. Predicting semantic descriptions from medical images with convolutional neural networks. Inf Process Med Imaging. 2015;24:437-448.
21. Zhang YG, Zhang BL, Coenen F, Xiao JM, Lu WJ. One-class kernel subspace ensemble for medical image classification. Eurasip J Adv Sig Pr. 2014;2014:1-13.
22. Srinivasan PP, Kim LA, Mettu PS, et al. Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images. Biomed Opt Express. 2014;5:3568-3577.
23. Farsiu S, Chiu SJ, O’Connell RV, et al. Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography. Ophthalmology. 2014;121:162-172.
24. Serrano-Aguilar P, Abreu R, Anton-Canalis L, et al. Development and validation of a computer-aided diagnostic tool to screen for age-related macular degeneration by optical coherence tomography. Br J Ophthalmol. 2012;96:503-507.
25. Liu YY, Ishikawa H, Chen M, et al. Computerized macular pathology diagnosis in spectral domain optical coherence tomography scans based on multiscale texture and shape features. Invest Ophthalmol Vis Sci. 2011;52:8316-8322.
26. Albarrak A, Coenen F, Zheng Y. Age-related macular degeneration identification in volumetric optical coherence tomography using decomposition and local feature extraction. Proc MIUA. 2013:59-64.
27. Lee K, Buitendijk GH, Bogunovic H, et al. Automated segmentability index for layer segmentation of macular SDOCT images. Trans Vis Sci Tech. 2016;5(2):14.
28. Waldstein SM, Gerendas BS, Montuoro A, Simader C, Schmidt-Erfurth U. Quantitative comparison of macular segmentation performance using identical retinal regions across multiple spectral-domain optical coherence tomography instruments. Br J Ophthalmol. 2015;99:794-800.
29. Venhuizen FG, van Ginneken B, Bloemen B, et al. Automated age-related macular degeneration classification in OCT using unsupervised feature learning. Proc SPIE. 2015: 94141I.
30. Fauser S, Smailhodzic D, Caramoy A, et al. Evaluation of serum lipid concentrations and genetic variants at high-density lipoprotein metabolism loci and TIMP3 in age-related macular degeneration. Invest Ophthalmol Vis Sci. 2011;52:5525-5528.
31. van de Ven JP, Smailhodzic D, Boon CJ, et al. Association analysis of genetic and environmental risk factors in the cuticular drusen subtype of age-related macular degeneration. Mol Vis. 2012;18:2271-2278.
32. Sivic J, Zisserman A. Efficient visual search of videos cast as text retrieval. IEEE Trans Pattern Anal Mach Intell. 2009;31: 591-606.
33. Avni U, Greenspan H, Konen E, Sharon M, Goldberger J. X-ray categorization and retrieval on the organ and pathology level, using patch-based visual words. IEEE Trans Med Imaging. 2011;30:733-746.
34. Leuschen JN, Schuman SG, Winter KP, et al. Spectral-domain optical coherence tomography characteristics of intermediate age-related macular degeneration. Ophthalmology. 2013;120: 140-150.
35. Romeny MH. Front-End Vision and Multi-Scale Image Analysis: Multi-scale Computer Vision Theory and Applications, Written in Mathematica. Dordrecht, The Netherlands: Springer; 2003.
36. Forgy EW. Cluster analysis of multivariate data: efficiency versus interpretability of classifications. Biometrics. 1965;21: 768-769.
37. Aly M, Munich M, Perona P. Multiple dictionaries for Bag of Words large scale image search. Conf Proc IEEE Int Imag Proc. 2011:1121-1123.
38. Pearson K. LIII: on lines and planes of closest fit to systems of points in space. Philosophical Magazine Series 6. 1901;2: 559-572.
39. Altman NS. An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat. 1992;46:175-185.
40. Tin Kam H. The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell. 1998; 20:832-844.
41. Rifkin R, Klautau A. In defense of one-vs-all classification. J Mach Learn Res. 2004;5:101-141.
42. Efron B. 1977 Rietz lecture: bootstrap methods: another look at the jackknife. Ann Stat. 1979;7:1-26.
43. Chen CL, Ishikawa H, Ling Y, et al. Signal normalization reduces systematic measurement differences between spectral-domain optical coherence tomography devices. Invest Ophthalmol Vis Sci. 2013;54:7317-7322.
44. Coates A, Lee H, Ng AY. An analysis of single-layer networks in unsupervised feature learning. AISTATS. 2011;15:215-223