Abstract

Optical coherence tomography (OCT) has proven to be an essential imaging modality for ophthalmology and is proving to be very important in neurology. OCT enables high resolution imaging of the retina, both at the optic nerve head and the macula. Macular retinal layer thicknesses provide useful diagnostic information and have been shown to correlate well with measures of disease severity in several diseases. Since manual segmentation of these layers is time consuming and prone to bias, automatic segmentation methods are critical for full utilization of this technology. In this work, we build a random forest classifier to segment eight retinal layers in macular cube images acquired by OCT. The random forest classifier learns the boundary pixels between layers, producing an accurate probability map for each boundary, which is then processed to finalize the boundaries. Using this algorithm, we can accurately segment the entire retina contained in the macular cube to an accuracy of at least 4.3 microns for any of the nine boundaries. Experiments were carried out on both healthy and multiple sclerosis subjects, with no difference in the accuracy of our algorithm found between the groups.

© 2013 OSA

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2013 (3)

S. Kirbas, K. Turkyilmaz, O. Anlar, A. Tufekci, and M. Durmus, “Retinal nerve fiber layer thickness in patients with Alzheimer disease,” J. Neuroophthalmol. 33, 58–61 (2013).
[Crossref]

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. De Zanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32, 531–543 (2013).
[Crossref]

A. Lang, A. Carass, E. Sotirchos, P. Calabresi, and J. L. Prince, “Segmentation of retinal OCT images using a random forest classifier,” Proc. SPIE 8669, 86690R (2013).
[Crossref]

2012 (2)

B. J. Antony, M. D. Abràmoff, M. Sonka, Y. H. Kwon, and M. K. Garvin, “Incorporation of texture-based features in optimal graph-theoretic approach with application to the 3-D segmentation of intraretinal surfaces in SD-OCT volumes,” Proc. SPIE 8314, 83141G (2012).
[Crossref]

S. Saidha, E. S. Sotirchos, M. A. Ibrahim, C. M. Crainiceanu, J. M. Gelfand, Y. J. Sepah, J. N. Ratchford, J. Oh, M. A. Seigo, S. D. Newsome, L. J. Balcer, E. M. Frohman, A. J. Green, Q. D. Nguyen, and P. A. Calabresi, “Microcystic macular oedema, thickness of the inner nuclear layer of the retina, and disease characteristics in multiple sclerosis: a retrospective study,” Lancet Neurol. 11, 963–972 (2012).
[Crossref] [PubMed]

2011 (4)

I. Ghorbel, F. Rossant, I. Bloch, S. Tick, and M. Paques, “Automated segmentation of macular layers in OCT images and quantitative evaluation of performances,” Pattern Recogn. 44, 1590–1603 (2011).
[Crossref]

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, 1743–1756 (2011).
[Crossref] [PubMed]

S. Saidha, S. B. Syc, M. A. Ibrahim, C. Eckstein, C. V. Warner, S. K. Farrell, J. D. Oakley, M. K. Durbin, S. A. Meyer, L. J. Balcer, E. M. Frohman, J. M. Rosenzweig, S. D. Newsome, J. N. Ratchford, Q. D. Nguyen, and P. A. Calabresi, “Primary retinal pathology in multiple sclerosis as detected by optical coherence tomography,” Brain 134, 518–533 (2011).
[Crossref] [PubMed]

R. F. Spaide and C. A. Curcio, “Anatomical correlates to the bands seen in the outer retina by optical coherence tomography: literature review and model,” Retina 31, 1609–1619 (2011).
[Crossref] [PubMed]

2010 (5)

2009 (6)

H. W. van Dijk, P. H. B. Kok, M. Garvin, M. Sonka, J. H. DeVries, R. P. J. Michels, M. E. J. van Velthoven, R. O. Schlingemann, F. D. Verbraak, and M. D. Abràmoff, “Selective loss of inner retinal layer thickness in type 1 diabetic patients with minimal diabetic retinopathy,” Invest. Ophthalmol. Visual Sci. 50, 3404–3409 (2009).
[Crossref]

A. Mishra, A. Wong, K. Bizheva, and D. A. Clausi, “Intra-retinal layer segmentation in optical coherence tomography images,” Opt. Express 17, 23719–23728 (2009).
[Crossref]

M. Garvin, M. Abramoff, X. Wu, S. Russell, T. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28, 1436–1447 (2009).
[Crossref] [PubMed]

J. G. Fujimoto, W. Drexler, J. S. Schuman, and C. K. Hitzenberger, “Optical coherence tomography (OCT) in ophthalmology: introduction,” Opt. Express 17, 3978–3979 (2009).
[Crossref] [PubMed]

M. E. Hajee, W. F. March, D. R. Lazzaro, A. H. Wolintz, E. M. Shrier, S. Glazman, and I. G. Bodis-Wollner, “Inner retinal layer thinning in Parkinson disease,” Arch. Ophthalmol. 127, 737–741 (2009).
[Crossref] [PubMed]

J. Huang, X. Liu, Z. Wu, H. Xiao, L. Dustin, and S. Sadda, “Macular thickness measurements in normal eyes with time-domain and fourier-domain optical coherence tomography,” Retina 29, 980–987 (2009).
[Crossref] [PubMed]

2008 (3)

M. Fleckenstein, P. C. Issa, H. Helb, S. Schmitz-Valckenberg, R. P. Finger, H. P. N. Scholl, K. U. Loeffler, and F. G. Holz, “High-resolution spectral domain-OCT imaging in geographic atrophy associated with age-related macular degeneration,” Invest. Ophthalmol. Visual Sci. 49, 4137–4144 (2008).
[Crossref]

E. M. Frohman, J. G. Fujimoto, T. C. Frohman, P. A. Calabresi, G. Cutter, and L. J. Balcer, “Optical coherence tomography: a window into the mechanisms of multiple sclerosis,” Nat. Clin. Pract. Neurol. 4, 664–675 (2008).
[Crossref] [PubMed]

M. Garvin, M. Abramoff, R. Kardon, S. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-D graph search,” IEEE Trans. Med. Imaging 27, 1495–1505 (2008).
[Crossref] [PubMed]

2007 (1)

S. B. Kotsiantis, “Supervised machine learning: a review of classification techniques,” Informatica 31, 249–268 (2007).

2006 (1)

K. Li, X. Wu, D. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images - a graph-theoretic approach,” IEEE Trans. Pattern Anal. Mach. Intell. 28, 119–134 (2006).
[Crossref] [PubMed]

2005 (2)

M. Varma and A. Zisserman, “A statistical approach to texture classification from single images,” Int. J. Comput. Vis. 62, 61–81 (2005).

H. Ishikawa, D. M. Stein, G. Wollstein, S. Beaton, J. G. Fujimoto, and J. S. Schuman, “Macular segmentation with optical coherence tomography,” Invest. Ophthalmol. Visual Sci. 46, 2012–2017 (2005).
[Crossref]

2004 (1)

Y. Boykov and V. Kolmogorov, “An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 1124–1137 (2004).
[Crossref]

2003 (2)

J.-M. Geusebroek, A. Smeulders, and J. van de Weijer, “Fast anisotropic Gauss filtering,” IEEE Trans. Image Process. 12, 938–943 (2003).
[Crossref]

V. Guedes, J. S. Schuman, E. Hertzmark, G. Wollstein, A. Correnti, R. Mancini, D. Lederer, S. Voskanian, L. Velazquez, H. M. Pakter, T. Pedut-Kloizman, J. G. Fujimoto, and C. Mattox, “Optical coherence tomography measurement of macular and nerve fiber layer thickness in normal and glaucomatous human eyes,” Ophthalmology 110, 177–189 (2003).
[Crossref] [PubMed]

2002 (1)

D. H. Anderson, R. F. Mullins, G. S. Hageman, and L. V. Johnson, “A role for local inflammation in the formation of drusen in the aging eye,” Am. J. Ophthalmol. 134, 411–431 (2002).
[Crossref] [PubMed]

2001 (2)

D. Koozekanani, K. Boyer, and C. Roberts, “Retinal thickness measurements from optical coherence tomography using a Markov boundary model,”IEEE Trans. Med. Imaging 20, 900–916 (2001).
[Crossref] [PubMed]

L. Breiman, “Random forests,” Mach. Learn. 45, 5–32 (2001).
[Crossref]

1999 (1)

R. E. Schapire and Y. Singer, “Improved boosting algorithms using confidence-rated predictions,” Mach. Learn. 37, 297–336 (1999).
[Crossref]

1995 (1)

C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn. 20, 273–297 (1995).
[Crossref]

1986 (1)

J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–698 (1986).
[Crossref] [PubMed]

1985 (1)

ETDRS Research Group, “Photocoagulation for diabetic macular edema. early treatment diabetic retinopathy study report number 1.” Arch. Ophthalmol. 103, 1796–1806 (1985).

Abdillahi, H.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. De Zanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imaging 32, 531–543 (2013).
[Crossref]

Abramoff, M.

M. Garvin, M. Abramoff, X. Wu, S. Russell, T. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28, 1436–1447 (2009).
[Crossref] [PubMed]

M. Garvin, M. Abramoff, R. Kardon, S. Russell, X. Wu, and M. Sonka, “Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-D graph search,” IEEE Trans. Med. Imaging 27, 1495–1505 (2008).
[Crossref] [PubMed]

Abràmoff, M. D.

B. J. Antony, M. D. Abràmoff, M. Sonka, Y. H. Kwon, and M. K. Garvin, “Incorporation of texture-based features in optimal graph-theoretic approach with application to the 3-D segmentation of intraretinal surfaces in SD-OCT volumes,” Proc. SPIE 8314, 83141G (2012).
[Crossref]

H. W. van Dijk, P. H. B. Kok, M. Garvin, M. Sonka, J. H. DeVries, R. P. J. Michels, M. E. J. van Velthoven, R. O. Schlingemann, F. D. Verbraak, and M. D. Abràmoff, “Selective loss of inner retinal layer thickness in type 1 diabetic patients with minimal diabetic retinopathy,” Invest. Ophthalmol. Visual Sci. 50, 3404–3409 (2009).
[Crossref]

Aguado, A. S.

M. S. Nixon and A. S. Aguado, Feature Extraction & Image Processing for Computer Vision, 3rd ed. (Academic Press, 2012).

Anderson, D. H.

D. H. Anderson, R. F. Mullins, G. S. Hageman, and L. V. Johnson, “A role for local inflammation in the formation of drusen in the aging eye,” Am. J. Ophthalmol. 134, 411–431 (2002).
[Crossref] [PubMed]

Anlar, O.

S. Kirbas, K. Turkyilmaz, O. Anlar, A. Tufekci, and M. Durmus, “Retinal nerve fiber layer thickness in patients with Alzheimer disease,” J. Neuroophthalmol. 33, 58–61 (2013).
[Crossref]

Antony, B. J.

B. J. Antony, M. D. Abràmoff, M. Sonka, Y. H. Kwon, and M. K. Garvin, “Incorporation of texture-based features in optimal graph-theoretic approach with application to the 3-D segmentation of intraretinal surfaces in SD-OCT volumes,” Proc. SPIE 8314, 83141G (2012).
[Crossref]

Araie, M.

Balcer, L. J.

S. Saidha, E. S. Sotirchos, M. A. Ibrahim, C. M. Crainiceanu, J. M. Gelfand, Y. J. Sepah, J. N. Ratchford, J. Oh, M. A. Seigo, S. D. Newsome, L. J. Balcer, E. M. Frohman, A. J. Green, Q. D. Nguyen, and P. A. Calabresi, “Microcystic macular oedema, thickness of the inner nuclear layer of the retina, and disease characteristics in multiple sclerosis: a retrospective study,” Lancet Neurol. 11, 963–972 (2012).
[Crossref] [PubMed]

S. Saidha, S. B. Syc, M. A. Ibrahim, C. Eckstein, C. V. Warner, S. K. Farrell, J. D. Oakley, M. K. Durbin, S. A. Meyer, L. J. Balcer, E. M. Frohman, J. M. Rosenzweig, S. D. Newsome, J. N. Ratchford, Q. D. Nguyen, and P. A. Calabresi, “Primary retinal pathology in multiple sclerosis as detected by optical coherence tomography,” Brain 134, 518–533 (2011).
[Crossref] [PubMed]

E. M. Frohman, J. G. Fujimoto, T. C. Frohman, P. A. Calabresi, G. Cutter, and L. J. Balcer, “Optical coherence tomography: a window into the mechanisms of multiple sclerosis,” Nat. Clin. Pract. Neurol. 4, 664–675 (2008).
[Crossref] [PubMed]

Beaton, S.

H. Ishikawa, D. M. Stein, G. Wollstein, S. Beaton, J. G. Fujimoto, and J. S. Schuman, “Macular segmentation with optical coherence tomography,” Invest. Ophthalmol. Visual Sci. 46, 2012–2017 (2005).
[Crossref]

Bizheva, K.

Bloch, I.

I. Ghorbel, F. Rossant, I. Bloch, S. Tick, and M. Paques, “Automated segmentation of macular layers in OCT images and quantitative evaluation of performances,” Pattern Recogn. 44, 1590–1603 (2011).
[Crossref]

Bodis-Wollner, I. G.

M. E. Hajee, W. F. March, D. R. Lazzaro, A. H. Wolintz, E. M. Shrier, S. Glazman, and I. G. Bodis-Wollner, “Inner retinal layer thinning in Parkinson disease,” Arch. Ophthalmol. 127, 737–741 (2009).
[Crossref] [PubMed]

Boyer, K.

D. Koozekanani, K. Boyer, and C. Roberts, “Retinal thickness measurements from optical coherence tomography using a Markov boundary model,”IEEE Trans. Med. Imaging 20, 900–916 (2001).
[Crossref] [PubMed]

Boykov, Y.

Y. Boykov and V. Kolmogorov, “An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 1124–1137 (2004).
[Crossref]

Breiman, L.

L. Breiman, “Random forests,” Mach. Learn. 45, 5–32 (2001).
[Crossref]

Burns, T.

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P. Jindahra, T. R. Hedges, C. E. Mendoza-Santiesteban, and G. T. Plant, “Optical coherence tomography of the retina: applications in neurology,” Curr. Opin. Neurol. 23, 16–23 (2010).
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B. J. Antony, M. D. Abràmoff, M. Sonka, Y. H. Kwon, and M. K. Garvin, “Incorporation of texture-based features in optimal graph-theoretic approach with application to the 3-D segmentation of intraretinal surfaces in SD-OCT volumes,” Proc. SPIE 8314, 83141G (2012).
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M. E. Hajee, W. F. March, D. R. Lazzaro, A. H. Wolintz, E. M. Shrier, S. Glazman, and I. G. Bodis-Wollner, “Inner retinal layer thinning in Parkinson disease,” Arch. Ophthalmol. 127, 737–741 (2009).
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Figures (10)

Fig. 1
Fig. 1

(a) A typical retinal OCT image (B-scan) enlarged with the layers labeled on the right-hand side. Every B-scan consists of a set of vertical scan lines (A-scans). The fovea is characterized by a depression in the surface of the retina where the top five (inner) layers are absent. (b) A fundus image with lines overlaid representing the locations of every B-scan within a volume. The red line corresponds to the B-scan in (a).

Fig. 2
Fig. 2

Flowchart of our algorithm. The RF+CAN result refers to the segmentation using the random forest (RF) boundary classification output with a Canny-inspired boundary tracking algorithm, while RF+GS refers to the result of using the RF output with an optimal graph search (GS) algorithm.

Fig. 3
Fig. 3

Row-wise: Shows two B-scans from within the same volume (a) with the original intensities, (b) after intensity normalization, (c) with the detected retinal boundary, and (d) after flattening.

Fig. 4
Fig. 4

Example images of the different types of features used by the classifier: (a) the relative distance between the bottom and top boundary with contour lines overlaid, (b) the average gradient in a neighborhood below each pixel, and anisotropic Gaussian (c) first and (d) second derivatives oriented at −10 (top), 0 (center), and 10 (bottom) degrees from the horizontal.

Fig. 5
Fig. 5

An example of the probabilities for each boundary generated as the output of the random forest classifier. The probabilities are shown for each boundary, starting from the top of the retina to the bottom, going across each row.

Fig. 6
Fig. 6

A plot of the mean absolute error across all boundary points vs. the number of subjects, Ns, used in training the classifier. For each value of Ns, the experiment was repeated with a random set of subjects ten times. Averages are across these ten trials and error bars represent one standard deviation.

Fig. 7
Fig. 7

(a,b) Images of the mean absolute error (μm) of each boundary at each pixel for the RF+CAN and RF+GS algorithms, respectively, with (c,d) the corresponding standard deviation of the errors. Averages are taken over all subjects and all cross-validation runs (280 values).

Fig. 8
Fig. 8

Box and whisker plots of the mean absolute errors for every subject used in this study. Subjects are ordered by diagnosis and then age (increasing from left to right within each diagnostic group). A total of 49 data points were used to generate each subject’s plot, with each data point representing the error of a particular B-scan averaged across all cross-validation runs. For each subject, the red line represents the median absolute error and the edges of the box correspond to the 25th and 75th percentile of the error. All points lying outside of the whiskers are greater than 1.5 times the interquartile range.

Fig. 9
Fig. 9

Two B-scan images from two different subjects are shown with the resulting boundaries from each of the 10 cross-validation runs overlaid. Each boundary is represented by a different color with the manual delineation shown atop the other boundaries in black. Therefore, if the color is not visible at a particular point, the automatic and manual segmentation are in agreement.

Fig. 10
Fig. 10

The template for the sectors of the macula overlaid on a fundus image. The dashed square surrounding the template represents the imaged area. The concentric circles are centered on the geometric center of the OCT volume and have diameters of 1 mm, 3 mm, and 6 mm.

Tables (3)

Tables Icon

Table 1 A Comparison of the Two Boundary Refinement Algorithms*

Tables Icon

Table 2 Mean Absolute and Signed Errors (and Standard Deviations) in μm for the RF+GS Refinement Algorithm on All Segmented Boundaries for All the Subjects and Broken-Down by Controls and MS Patients

Tables Icon

Table 3 Retinal Layer Thickness Absolute Errors (in μm, with Standard Deviation) Calculated for Different Sectors of the Macula (See Fig. 10 for Sector Positions)*

Equations (1)

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e r , l = 1 | S | i S | w ¯ auto , i r , l w ¯ true , i r , l | , r { C 1 , S 3 , , M } l { RNFL , , RPE } ,

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