FloatingCanvas: quantification of 3D retinal structures from spectral-domain optical coherence tomography

Haogang Zhu; David P. Crabb; Patricio G. Schlottmann; Tuan Ho; David F. Garway-Heath

doi:10.1364/OE.18.024595

FloatingCanvas: quantification of 3D retinal structures from spectral-domain optical coherence tomography

Haogang Zhu, David P. Crabb, Patricio G. Schlottmann, Tuan Ho, and David F. Garway-Heath

Optics Express
Vol. 18,
Issue 24,
pp. 24595-24610
(2010)
•https://doi.org/10.1364/OE.18.024595

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Haogang Zhu, David P. Crabb, Patricio G. Schlottmann, Tuan Ho, and David F. Garway-Heath, "FloatingCanvas: quantification of 3D retinal structures from spectral-domain optical coherence tomography," Opt. Express 18, 24595-24610 (2010)

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Related Topics
Table of Contents Category
- Medical Optics and Biotechnology
Optics & Photonics Topics
?

The topics in this list come from the OSA Optics and Photonics Topics applied to this article.
Previously assigned OCIS codes
- Image processing (100.0100)
- Ophthalmology (170.4470)
- Optical coherence tomography (170.4500)
- Optical diagnostics for medicine (170.4580)

About this Article
History
- Original Manuscript: August 30, 2010
- Revised Manuscript: October 7, 2010
- Manuscript Accepted: October 10, 2010
- Published: November 10, 2010

Accessible

Open Access

Abstract

Spectral-domain optical coherence tomography (SD-OCT) provides volumetric images of retinal structures with unprecedented detail. Accurate segmentation algorithms and feature quantification in these images, however, are needed to realize the full potential of SD-OCT. The fully automated segmentation algorithm, FloatingCanvas, serves this purpose and performs a volumetric segmentation of retinal tissue layers in three-dimensional image volume acquired around the optic nerve head without requiring any pre-processing. The reconstructed layers are analyzed to extract features such as blood vessels and retinal nerve fibre layer thickness. Findings from images obtained with the RTVue-100 SD-OCT (Optovue, Fremont, CA, USA) indicate that FloatingCanvas is computationally efficient and is robust to the noise and low contrast in the images. The FloatingCanvas segmentation demonstrated good agreement with the human manual grading. The retinal nerve fibre layer thickness maps obtained with this method are clinically realistic and highly reproducible compared with time-domain StratusOCT^TM.

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References

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G. Quellec, K. Lee, M. Dolejsi, M. K. Garvin, M. D. Abràmoff, and M. Sonka, “Three-dimensional analysis of retinal layer texture: identification of fluid-filled regions in SD-OCT of the macula,” IEEE Trans. Med. Imaging 29(6), 1321–1330 (2010).
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[Crossref]

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[Crossref] [PubMed]

2009 (6)

M. K. Garvin, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imaging 28(9), 1436–1447 (2009).
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[Crossref] [PubMed]

T. Fabritius, S. Makita, M. Miura, R. Myllylä, and Y. Yasuno, “Automated segmentation of the macula by optical coherence tomography,” Opt. Express 17(18), 15659–15669 (2009).
[Crossref] [PubMed]

A. Mishra, A. Wong, K. Bizheva, and D. A. Clausi, “Intra-retinal layer segmentation in optical coherence tomography images,” Opt. Express 17(26), 23719–23728 (2009).
[Crossref]

2008 (4)

M. K. Garvin, M. D. Abramoff, R. Kardon, S. R. 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(10), 1495–1505 (2008).
[Crossref] [PubMed]

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[Crossref]

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[Crossref] [PubMed]

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[Crossref] [PubMed]

2007 (2)

M. Haeker, M. Sonka, R. Kardon, V. A. Shah, X. Wu, and M Abramoff, “Automated segmentation of intraretinal layers from macular optical coherence tomography images,” Proc. SPIE 6512, 651214 (2007).
[Crossref]

M. E. van Velthoven, D. J. Faber, F. D. Verbraak, T. G. van Leeuwen, and M. D. de Smet, “Recent developments in optical coherence tomography for imaging the retina,” Prog. Retin. Eye Res. 26(1), 57–77 (2007).
[Crossref]

2006 (1)

K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images--a graph-theoretic approach,” IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 119–134 (2006).
[Crossref] [PubMed]

2005 (6)

S. Jiao, R. Knighton, X. Huang, G. Gregori, and C. Puliafito, “Simultaneous acquisition of sectional and fundus ophthalmic images with spectral-domain optical coherence tomography,” Opt. Express 13(2), 444–452 (2005).
[Crossref] [PubMed]

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[Crossref] [PubMed]

R. R. Bourne, F. A. Medeiros, C. Bowd, K. Jahanbakhsh, L. M. Zangwill, and R. N. Weinreb, “Comparability of retinal nerve fiber layer thickness measurements of optical coherence tomography instruments,” Invest. Ophthalmol. Vis. Sci. 46(4), 1280–1285 (2005).
[Crossref] [PubMed]

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).
[Crossref] [PubMed]

D. Cabrera Fernández, H. M. Salinas, and C. A. Puliafito, “Automated detection of retinal layer structures on optical coherence tomography images,” Opt. Express 13(25), 10200–10216 (2005).
[Crossref] [PubMed]

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[Crossref] [PubMed]

2004 (1)

N. Nassif, B. Cense, B. Hyle Park, S. H. Yun, T. C. Chen, B. E. Bouma, G. J. Tearney, and J. F. de Boer, “In vivo human retinal imaging by ultrahigh-speed spectral domain optical coherence tomography,” Opt. Lett. 29(5), 480–482 (2004).
[Crossref] [PubMed]

2003 (4)

R. Leitgeb, C. K. Hitzenberger, and A. F. Fercher, “Performance of fourier domain vs. time domain optical coherence tomography,” Opt. Express 11(8), 889–894 (2003).
[Crossref] [PubMed]

J. F. de Boer, B. Cense, B. H. Park, M. C. Pierce, G. J. Tearney, and B. E. Bouma, “Improved signal-to-noise ratio in spectral-domain compared with time-domain optical coherence tomography,” Opt. Lett. 28(21), 2067–2069 (2003).
[Crossref] [PubMed]

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(1), 177–189 (2003).
[Crossref] [PubMed]

P. Carpineto, M. Ciancaglini, E. Zuppardi, G. Falconio, E. Doronzo, and L. Mastropasqua, “Reliability of nerve fiber layer thickness measurements using optical coherence tomography in normal and glaucomatous eyes,” Ophthalmology 110(1), 190–195 (2003).
[Crossref] [PubMed]

2002 (1)

M. Wojtkowski, R. Leitgeb, A. Kowalczyk, T. Bajraszewski, and A. F. Fercher, “In vivo human retinal imaging by Fourier domain optical coherence tomography,” J. Biomed. Opt. 7(3), 457–463 (2002).
[Crossref] [PubMed]

2001 (1)

D. Koozekanani, K. Boyer, and C. Roberts, “Retinal thickness measurements from optical coherence tomography using a Markov boundary model,” IEEE Trans. Med. Imaging 20(9), 900–916 (2001).
[Crossref] [PubMed]

2000 (1)

N. V. Swindale, G. Stjepanovic, A. Chin, and F. S. Mikelberg, “Automated analysis of normal and glaucomatous optic nerve head topography images,” Invest. Ophthalmol. Vis. Sci. 41(7), 1730–1742 (2000).
[PubMed]

1995 (3)

J. S. Schuman, M. R. Hee, A. V. Arya, T. Pedut-Kloizman, C. A. Puliafito, J. G. Fujimoto, and E. A. Swanson, “Optical coherence tomography: a new tool for glaucoma diagnosis,” Curr. Opin. Ophthalmol. 6(2), 89–95 (1995).
[PubMed]

M. R. Hee, J. A. Izatt, E. A. Swanson, D. Huang, J. S. Schuman, C. P. Lin, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography of the human retina,” Arch. Ophthalmol. 113(3), 325–332 (1995).
[PubMed]

A. F. Fercher, C. K. Hitzenberger, G. Kamp, and S. Y. El-Zaiat, “Measurement of intraocular distances by backscattering spectral interferometry,” Opt. Commun. 117(1-2), 43–48 (1995).
[Crossref]

1991 (1)

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254(5035), 1178–1181 (1991).
[Crossref] [PubMed]

1965 (1)

G. Golub and W. Kahan, “Calculating the singular value and pseudo-inverse of a matrix,” SIAM Numerical Analysis 63, 205–224 (1965).

Abramoff, M

Abramoff, M. D.

Abràmoff, M. D.

Ahlers, C.

Akkin, T.

Arthur, S. N.

Arya, A. V.

Bajraszewski, T.

Baumann, B.

Beaton, S.

Bilonick, R. A.

Bizheva, K.

A. Mishra, A. Wong, K. Bizheva, and D. A. Clausi, “Intra-retinal layer segmentation in optical coherence tomography images,” Opt. Express 17(26), 23719–23728 (2009).
[Crossref]

Bouma, B. E.

Bourne, R. R.

Bowd, C.

Boyer, K.

Budenz, D. L.

Burns, T. L.

Cabrera Fernández, D.

Carpineto, P.

Cense, B.

Chan, R.

Chang, R. T.

Chang, W.

Chen, D. Z.

Chen, T.

Chen, T. C.

Chin, A.

Chiu, S. J.

Ciancaglini, M.

Clausi, D. A.

A. Mishra, A. Wong, K. Bizheva, and D. A. Clausi, “Intra-retinal layer segmentation in optical coherence tomography images,” Opt. Express 17(26), 23719–23728 (2009).
[Crossref]

Correnti, A.

Dastmalchi, S.

de Boer, J.

de Boer, J. F.

de Smet, M. D.

Dolejsi, M.

Doronzo, E.

Duker, J. S.

El-Zaiat, S. Y.

A. F. Fercher, C. K. Hitzenberger, G. Kamp, and S. Y. El-Zaiat, “Measurement of intraocular distances by backscattering spectral interferometry,” Opt. Commun. 117(1-2), 43–48 (1995).
[Crossref]

Faber, D. J.

Fabritius, T.

Falconio, G.

Farsiu, S.

Fercher, A. F.

R. Leitgeb, C. K. Hitzenberger, and A. F. Fercher, “Performance of fourier domain vs. time domain optical coherence tomography,” Opt. Express 11(8), 889–894 (2003).
[Crossref] [PubMed]

A. F. Fercher, C. K. Hitzenberger, G. Kamp, and S. Y. El-Zaiat, “Measurement of intraocular distances by backscattering spectral interferometry,” Opt. Commun. 117(1-2), 43–48 (1995).
[Crossref]

Flotte, T.

Fortune, B.

Fujimoto, J. G.

Gabriele, M. L.

Garvin, M. K.

Geitzenauer, W.

Golub, G.

G. Golub and W. Kahan, “Calculating the singular value and pseudo-inverse of a matrix,” SIAM Numerical Analysis 63, 205–224 (1965).

Götzinger, E.

Gregori, G.

Gregory, K.

Guedes, V.

Haeker, M.

Hee, M. R.

Hertzmark, E.

Hitzenberger, C. K.

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Fig. 1

Steps in the deformation procedure to search for the anterior RNFL boundary. (a-c) a B-scan in an image volume superimposed with the corresponding curve in the analytical surface at the 30th, 45th and the last (96th) iterations of the deformation procedure. Plates (d-f) show an overview of the analytical surface at these 3 iterations. The arrow in (a) shows an area with strong gradient that sets the Θ function to be 1 before the analytical surface is close to the target boundary.

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Fig. 2

En-face image and pixel vesselness. (a) the en-face image calculated by averaging the 50 pixels below and above the detected RPE. (b) the pixel vesselness in grayscale.

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Fig. 4

The RNFLT map calculated from the segmented retinal structures of a healthy subject (a) and a glaucomatous patient (b). The RNFLT map was colour-coded in micrometers. Note the significantly thicker RNFLT, especially in the superior and inferior areas, in the healthy example (a) compared with that of the glaucomatous example (b).

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Fig. 3

An illustration of the location and width of two different annuli used to calculate the mean and quadrant RNFLT. The wide (0.580mm wide from the inner margin radius of 1.170mm) annulus was twice as wide as the narrow one (0.290mm wide from the inner margin radius of 1.315mm). Both annuli were centered on the same circle with a radius of 1.460mm.

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Fig. 5

The benefit of a segmentation in 3D space. (a) a B-scan with a segment of RNFL with an indistinct posterior boundary. The region with an indistinct posterior RNFL boundary is denoted by a white arrow in the image. (b, c) the RNFL boundary in the B-scans adjacent to the one in (a) is more distinct; the location of indistinct RNFL boundary in (a) is also marked. (d) the locations of B-scans (a, b, c) in the image volume. (e) the segmented RPE and anterior and posterior RNFL boundaries in image (a) by FloatingCanvas.

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Tables (4)

Table 1 Mean and SD of signed and absolute difference between the manual and FloatingCanvas segmentation for glaucomatous and healthy subjects. The difference values were summarized with all 3 surfaces and anterior, posterior RNFL and RPE respectively

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Table 2 Mean and SD of total and quadrant retinal nerve fiber layer thickness (RNFLT) of healthy and glaucomatous eyes. RNFLT was determined with two types of calculation annuli (0.58mm and 0.29mm wide, respectively)

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Table 3 Coefficient of variation and test-retest variability of total and quadrant retinal nerve fiber layer thickness (RNFLT) of healthy retina measured by FloatingCanvas. RNFLT was calculated at two widths of calculation annuli (0.58mm and 0.29mm). A typical reproducibility of StratusOCT is given for comparison

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Table 4 Coefficient of variation and test-retest variability of total and quadrant retinal nerve fiber layer thickness (RNFLT) of glaucomatous subjects measured by FloatingCanvas. RNFLT was calculated at two widths of calculation annuli (0.58mm and 0.29mm). A typical reproducibility of StratusOCT is given for comparison

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Equations (16)

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[\begin{matrix} w \\ f (X_{*}) \end{matrix}] ~ N (0, [\begin{matrix} K (X, X) + δ_{n}^{2} I & K (X, X_{*}) \\ K (X_{*}, X) & K (X_{*}, X_{*}) \end{matrix}])

\bar{f} (X_{*}) = K (X_{*}, X) {(K (X, X) + δ_{n}^{2} I)}^{- 1} w

cov(\bar{f} (X_{*})) = K (X_{*}, X_{*}) - K (X_{*}, X) {(K (X, X) + δ_{n}^{2} I)}^{- 1} K (X, X_{*})

\frac{d \bar{f} (X_{*})}{d t} = \bar{Θ} (X_{*}, \bar{f} (X_{*})) F_{g} (X_{*}, \bar{f} (X_{*})) + Θ (X_{*}, \bar{f} (X_{*})) F_{I m g} (X_{*}, \bar{f} (X_{*}))

K (X_{*}, X) {(K (X, X) + δ_{n}^{2} I)}^{- 1} \frac{d w}{d t} = \bar{Θ} F_{g} + Θ F_{I m g}

\frac{d w}{d t} = Λ^{†} (\bar{Θ} F_{g} + Θ F_{I m g})

w^{n e w} = w^{o l d} + Λ^{†} ({\bar{Θ}}^{o l d} F_{g}^{o l d} + Θ^{o l d} F_{I m g}^{o l d}) Δ t

F_{g} (x_{* i}, \bar{f} (x_{* i})) = (\nabla I m (x_{* i}, \bar{f} (x_{* i})) + g) • \nabla \bar{f} (x_{* i})

F_{i m g} (x_{* i}, \bar{f} (x_{* i})) = - \nabla | \nabla I m (x_{* i}, \bar{f} (x_{* i})) | • \nabla \bar{f} (x_{* i})

Θ (x_{* i}, \bar{f} (x_{* i})) = {\begin{matrix} 1 & | \nabla I m (x_{* i}, \bar{f} (x_{* i})) | \geq | g | \\ 0 & o t h e r w i s e \end{matrix}

F_{g} (x_{* i}, \bar{f} (x_{* i})) = - | g |

F_{i m g} (x_{* i}, \bar{f} (x_{* i})) = - \nabla I m (x_{* i}, \bar{f} (x_{* i})) • \nabla \bar{f} (x_{* i})

Θ (x_{* i}, \bar{f} (x_{* i})) = {\begin{matrix} 1 & I m (x_{* i}, \bar{f} (x_{* i})) \leq I m_{\max} (x_{* i}) \\ 0 & o t h e r w i s e \end{matrix}

Θ (x_{* i}, \bar{f} (x_{* i})) = {\begin{matrix} 1 & | \nabla I m (x_{* i}, \bar{f} (x_{* i})) | \geq | g | and both constraints are met \\ 0 & o t h e r w i s e \end{matrix}

S_{v n} = {\begin{matrix} 0 v_{\max} \leq 0 \\ \frac{e^{- \frac{{(ν_{\min} / ν_{\max})}^{2}}{{0.5}^{2}}}}{e^{\frac{1}{2}} - e^{- \frac{ν_{\min}^{2} + ν_{\max}^{2}}{15^{2}}}} v_{\max} > 0 \end{matrix}

[\begin{matrix} \nabla_{x_{i}^{1} x_{i}^{1}} E F (x_{i}) & \nabla_{x_{i}^{1} x_{i}^{2}} E F (x_{i}) \\ \nabla_{x_{i}^{2} x_{i}^{1}} E F (x_{i}) & \nabla_{x_{i}^{2} x_{i}^{2}} E F (x_{i}) \end{matrix}]

	Glaucoma (MEAN ± SD, µm)		Healthy (MEAN ± SD, µm)
Surface	Signed	Absolute	Signed	Absolute
All surfaces	−0.8 ± 2.1	4.8 ± 2.7	−0.7 ± 2.3	3.6 ± 2.1
Anterior RNFL	1.2 ± 0.7	3.3 ± 0.9	1.5 ± 0.9	2.2 ± 0.7
Posterior RNFL	−1.9 ± 1.8	6.8 ± 1.8	−1.7 ± 2.5	4.7 ± 1.4
RPE	−1.6 ± 2.0	4.3 ± 1.8	−1.8 ± 1.6	3.5 ± 1.9

	Glaucoma (MEAN ± SD, µm)		Healthy (MEAN ± SD, µm)
Calculation circle	0.58mm	0.29mm	0.58mm	0.29mm
Total RNFLT	86 ± 16	85 ± 18	110 ± 6	111 ± 7
Temporal RNFLT	71 ± 16	70 ± 13	77 ± 16	77 ± 14
Superior RNFLT	98 ± 21	97 ± 22	133 ± 15	134 ± 12
Nasal RNFLT	76 ± 16	75 ± 16	96 ± 14	97 ± 13
Inferior RNFLT	99 ± 30	98 ± 32	134 ± 16	135 ± 15

	0.58mm annulus		0.29mm annulus		Stratus OCT [38]
	CV(%)	Test-retest variability (µm)	CV(%)	Test-retest variability (µm)	CV(%)	Test-retest variability (µm)
Total RNFLT	1.5	3.3	1.5	3.3	1.7	3.5
Temporal RNFLT	3.8	5.8	3.9	6.0	5.1	7.5
Superior RNFLT	2.6	7.5	3.0	8.2	3.8	9.6
Nasal RNFLT	3.5	6.6	4.3	8.0	6.7	10.2
Inferior RNFLT	1.5	4.4	1.6	4.6	3.7	9.7

	0.58mm annulus		0.29mm annulus		Stratus OCT [38]
	CV(%)	Test-retest variability (µm)	CV(%)	Test-retest variability (µm)	CV(%)	Test-retest variability (µm)
Total RNFLT	2.2	4.0	2.2	4.1	3.7	5.2
Temporal RNFLT	4.5	5.2	5.0	5.6	5.3	5.6
Superior RNFLT	3.8	7.8	4.2	8.6	6.4	10.7
Nasal RNFLT	4.9	6.7	6.1	7.4	9.0	10.2
Inferior RNFLT	2.1	4.7	1.9	4.3	6.6	10.6

	Glaucoma (MEAN ± SD, µm)		Healthy (MEAN ± SD, µm)
Surface	Signed	Absolute	Signed	Absolute
All surfaces	−0.8 ± 2.1	4.8 ± 2.7	−0.7 ± 2.3	3.6 ± 2.1
Anterior RNFL	1.2 ± 0.7	3.3 ± 0.9	1.5 ± 0.9	2.2 ± 0.7
Posterior RNFL	−1.9 ± 1.8	6.8 ± 1.8	−1.7 ± 2.5	4.7 ± 1.4
RPE	−1.6 ± 2.0	4.3 ± 1.8	−1.8 ± 1.6	3.5 ± 1.9