Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology

Biomedical Optics Express

Volumn 5, Issue 2, 2014, Pages 348-365

  Srinivasan, Pratul P ^a,c   Heflin, Stephanie J ^b   Izatt, Joseph A ^a,b   Arshavsky, Vadim Y ^b   Farsiu, Sina ^a,b,c  

^a Duke University   (United States)

^b DUKE UNIVERSITY MEDICAL CENTER   (United States)

^c Duke University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GRAPH THEORY; MAMMALS; OPHTHALMOLOGY; OPTICAL TOMOGRAPHY; PATHOLOGY;

ACCURATE PERFORMANCE; ACCURATE QUANTIFICATIONS; AUTOMATIC APPROACHES; AUTOMATIC SEGMENTATIONS; LAYER BOUNDARIES; MANUAL SEGMENTATION; NEUROLOGICAL DISEASE; SPECTRAL DOMAIN OCT;

IMAGE SEGMENTATION;

ANIMAL EXPERIMENT; ARTICLE; AUTOMATIC SEGMENTATION; BLOOD VESSEL; COMPUTER PROGRAM; IMAGE ENHANCEMENT; IMAGING; MEASUREMENT ACCURACY; NOISE; NONHUMAN; OPTIC NERVE; RETINA; RETINA DEGENERATION; RETINAL NERVE FIBER LAYER THICKNESS; SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHY;

EID: 84893591763     PISSN: None     EISSN: 21567085     Source Type: Journal    
DOI: 10.1364/BOE.5.000348     Document Type: Article

References (37)

1. L. R. Ferguson, J. M. Dominguez II, S. Balaiya, S. Grover, and K. V. Chalam, "Retinal Thickness Normative Data in Wild-Type Mice Using Customized Miniature SD-OCT, " PLoS ONE 8(6), e67265 (2013).
2. V. J. Srinivasan, T. H. Ko, M. Wojtkowski, M. Carvalho, A. Clermont, S.-E. Bursell, Q. H. Song, J. Lem, J. S. Duker, J. S. Schuman, and J. G. Fujimoto, "Noninvasive Volumetric Imaging and Morphometry of the Rodent Retina with High-Speed, Ultrahigh-Resolution Optical Coherence Tomography, " Invest. Ophthalmol. Vis. Sci. 47(12), 5522-5528 (2006).
3. O. P. Kocaoglu, S. R. Uhlhorn, E. Hernandez, R. A. Juarez, R. Will, J.-M. Parel, and F. Manns, "Simultaneous Fundus Imaging and Optical Coherence Tomography of the Mouse Retina, " Invest. Ophthalmol. Vis. Sci. 48(3), 1283-1289 (2007).
4. S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, "Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation, " Opt. Express 18(18), 19413-19428 (2010).
5. S. J. Chiu, J. A. Izatt, R. V. O'Connell, K. P. Winter, C. A. Toth, and S. Farsiu, "Validated Automatic Segmentation of AMD Pathology Including Drusen and Geographic Atrophy in SD-OCT Images, " Invest. Ophthalmol. Vis. Sci. 53(1), 53-61 (2012).
6. D. C. DeBuc, "A review of algorithms for segmentation of retinal image data using optical coherence tomography, " Image Segmentation, P.-G. Ho, ed. (2011), pp. 15-54.
7. D. C. DeBuc, G. M. Somfai, S. Ranganathan, E. Tátrai, M. Ferencz, and C. A. Puliafito, "Reliability and reproducibility of macular segmentation using a custom-built optical coherence tomography retinal image analysis software, " J. Biomed. Opt. 14(6), 064023 (2009).
8. G. Gregori, F. Wang, P. J. Rosenfeld, Z. Yehoshua, N. Z. Gregori, B. J. Lujan, C. A. Puliafito, and W. J. Feuer, "Spectral domain optical coherence tomography imaging of drusen in nonexudative age-related macular degeneration, " Ophthalmology 118(7), 1373-1379 (2011).
9. V. Kajić, M. Esmaeelpour, C. Glittenberg, M. F. Kraus, J. Honegger, R. Othara, S. Binder, J. G. Fujimoto, and W. Drexler, "Automated three-dimensional choroidal vessel segmentation of 3D 1060 nm OCT retinal data, " Biomed. Opt. Express 4(1), 134-150 (2013).
10. R. J. Zawadzki, A. R. Fuller, D. F. Wiley, B. Hamann, S. S. Choi, and J. S. Werner, "Adaptation of a support vector machine algorithm for segmentation and visualization of retinal structures in volumetric optical coherence tomography data sets, " J. Biomed. Opt. 12(4), 041206 (2007).
11. H. Ishikawa, D. M. Stein, G. Wollstein, S. Beaton, J. G. Fujimoto, and J. S. Schuman, "Macular Segmentation with Optical Coherence Tomography, " Invest. Ophthalmol. Vis. Sci. 46(6), 2012-2017 (2005).
12. A. Lang, A. Carass, M. Hauser, E. S. Sotirchos, P. A. Calabresi, H. S. Ying, and J. L. Prince, "Retinal layer segmentation of macular OCT images using boundary classification, " Biomed. Opt. Express 4(7), 1133-1152 (2013).
13. J. Tian, P. Marziliano, M. Baskaran, T. A. Tun, and T. Aung, "Automatic segmentation of the choroid in enhanced depth imaging optical coherence tomography images, " Biomed. Opt. Express 4(3), 397-411 (2013).
14. M. Ruggeri, H. Wehbe, S. Jiao, G. Gregori, M. E. Jockovich, A. Hackam, Y. Duan, and C. A. Puliafito, "In Vivo Three-Dimensional High-Resolution Imaging of Rodent Retina with Spectral-Domain Optical Coherence Tomography, " Invest. Ophthalmol. Vis. Sci. 48(4), 1808-1814 (2007).
15. J. Molnár, D. Chetverikov, D. Cabrera DeBuc, W. Gao, and G. Somfai, "Layer extraction in rodent retinal images acquired by optical coherence tomography, " Mach. Vis. Appl. 23(6), 1129-1139 (2012).
16. A. Yazdanpanah, G. Hamarneh, B. R. Smith, and M. V. Sarunic, "Segmentation of Intra-Retinal Layers From Optical Coherence Tomography Images Using an Active Contour Approach, " IEEE Trans. Med. Imaging 30(2), 484-496 (2011).
17. B. J. Antony, M. D. Abràmoff, M. M. Harper, W. Jeong, E. H. Sohn, Y. H. Kwon, R. Kardon, and M. K. Garvin, "A combined machine-learning and graph-based framework for the segmentation of retinal surfaces in SD-OCT volumes, " Biomed. Opt. Express 4(12), 2712-2728 (2013).
18. J. Y. Lee, S. J. Chiu, P. Srinivasan, J. A. Izatt, C. A. Toth, S. Farsiu, and G. J. Jaffe, "Fully Automatic Software for Quantification of Retinal Thickness and Volume in Eyes with Diabetic Macular Edema from Images Acquired by Cirrus and Spectralis Spectral Domain Optical Coherence Tomography Machines, " Invest. Ophthalmol. Vis. Sci. 54, 7595-7602 (2013).
19. F. LaRocca, S. J. Chiu, R. P. McNabb, A. N. Kuo, J. A. Izatt, and S. Farsiu, "Robust automatic segmentation of corneal layer boundaries in SDOCT images using graph theory and dynamic programming, " Biomed. Opt. Express 2(6), 1524-1538 (2011).
20. M. A. Hearst, S. T. Dumais, E. Osman, J. Platt, and B. Scholkopf, "Support vector machines, " IEEE Intell. Syst. Appl. 13, 18-28 (1998).
21. A. R. Fuller, R. J. Zawadzki, S. Choi, D. F. Wiley, J. S. Werner, and B. Hamann, "Segmentation of Threedimensional Retinal Image Data, " IEEE Trans. Vis. Comput. Graph. 13(6), 1719-1726 (2007).
22. K. A. Vermeer, J. van der Schoot, H. G. Lemij, and J. F. de Boer, "Automated segmentation by pixel classification of retinal layers in ophthalmic OCT images, " Biomed. Opt. Express 2(6), 1743-1756 (2011).
23. Z. Burgansky-Eliash, G. Wollstein, T. Chu, J. D. Ramsey, C. Glymour, R. J. Noecker, H. Ishikawa, and J. S. Schuman, "Optical Coherence Tomography Machine Learning Classifiers for Glaucoma Detection: A Preliminary Study, " Invest. Ophthalmol. Vis. Sci. 46(11), 4147-4152 (2005).
24. C. Bowd, J. Hao, I. M. Tavares, F. A. Medeiros, L. M. Zangwill, T.-W. Lee, P. A. Sample, R. N. Weinreb, and M. H. Goldbaum, "Bayesian Machine Learning Classifiers for Combining Structural and Functional Measurements to Classify Healthy and Glaucomatous Eyes, " Invest. Ophthalmol. Vis. Sci. 49(3), 945-953 (2008).
25. D. Bizios, A. Heijl, and B. Bengtsson, "Trained Artificial Neural Network for Glaucoma Diagnosis Using Visual Field Data: A Comparison With Conventional Algorithms, " J. Glaucoma 16(1), 20-28 (2007).
26. E. Galilea, G. Santos-García, and I. Suárez-Bárcena, "Identification of Glaucoma Stages with Artificial Neural Networks Using Retinal Nerve Fibre Layer Analysis and Visual Field Parameters, " Innovations in Hybrid Intelligent Systems, E. Corchado, J. Corchado, and A. Abraham, eds. (Springer Berlin Heidelberg, 2007), pp. 418-424.
27. D. Bizios, A. Heijl, J. L. Hougaard, and B. Bengtsson, "Machine learning classifiers for glaucoma diagnosis based on classification of retinal nerve fibre layer thickness parameters measured by Stratus OCT, " Acta Ophthalmol. (Copenh.) 88(1), 44-52 (2010).
28. M. D. Abramoff, M. K. Garvin, and M. Sonka, "Retinal Imaging and Image Analysis, " IEEE Rev. Biomed. Eng. 3, 169-208 (2010).
29. L. Fang, S. Li, Q. Nie, J. A. Izatt, C. A. Toth, and S. Farsiu, "Sparsity based denoising of spectral domain optical coherence tomography images, " Biomed. Opt. Express 3(5), 927-942 (2012).
30. M. A. Mayer, A. Borsdorf, M. Wagner, J. Hornegger, C. Y. Mardin, and R. P. Tornow, "Wavelet denoising of multiframe optical coherence tomography data, " Biomed. Opt. Express 3(3), 572-589 (2012).
31. L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, and S. Farsiu, "Fast Acquisition and Reconstruction of Optical Coherence Tomography Images via Sparse Representation, " IEEE Trans. Med. Imaging 32(11), 2034-2049 (2013).
32. K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, "Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering, " IEEE Trans. Image Process. 16(8), 2080-2095 (2007).
33. R. Estrada, C. Tomasi, M. T. Cabrera, D. K. Wallace, S. F. Freedman, and S. Farsiu, "Exploratory Dijkstra forest based automatic vessel segmentation: applications in video indirect ophthalmoscopy (VIO), " Biomed. Opt. Express 3(2), 327-339 (2012).
34. R. Estrada, C. Tomasi, M. T. Cabrera, D. K. Wallace, S. F. Freedman, and S. Farsiu, "Enhanced video indirect ophthalmoscopy (VIO) via robust mosaicing, " Biomed. Opt. Express 2(10), 2871-2887 (2011).
35. J. Lem, N. V. Krasnoperova, P. D. Calvert, B. Kosaras, D. A. Cameron, M. Nicolò, C. L. Makino, and R. L. Sidman, "Morphological, physiological, and biochemical changes in rhodopsin knockout mice, " Proc. Natl. Acad. Sci. U.S.A. 96(2), 736-741 (1999).
36. E. S. Lobanova, S. Finkelstein, N. P. Skiba, and V. Y. Arshavsky, "Proteasome overload is a common stress factor in multiple forms of inherited retinal degeneration, " Proc. Natl. Acad. Sci. U.S.A. 110(24), 9986-9991 (2013).
37. R. Barhoum, G. Martínez-Navarrete, S. Corrochano, F. Germain, L. Fernandez-Sanchez, E. J. de la Rosa, P. de la Villa, and N. Cuenca, "Functional and structural modifications during retinal degeneration in the rd10 mouse, " Neuroscience 155(3), 698-713 (2008).