OSA's Digital Library

Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 18, Iss. 24 — Nov. 22, 2010
  • pp: 24595–24610
« Show journal navigation

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  »View Author Affiliations


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


View Full Text Article

Acrobat PDF (1618 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

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 StratusOCTTM.

© 2010 OSA

1. Introduction

Optical Coherence Tomography (OCT) has been widely used as a tool for evaluating the structure of the retina in cross-section [1

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]

,2

2. 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]

]. Time-domain OCT has been used in glaucoma diagnosis and follow-up by determining retinal nerve fibre layer thickness (RNFLT) since it was adopted in the clinical setting [3

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]

7

7. 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]

]. Because of its limited speed, time-domain OCT only provides RNFLT measurements in a line scan, generally a peripapillary circle but does not provide a three-dimensional (3D) RNFLT map.

The newly developed and commercialized spectral-domain OCT (SD-OCT) [8

8. 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]

,9

9. 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]

] provides much faster scans [10

10. 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]

] with improved signal-to-noise ratio [11

11. 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]

,12

12. 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]

] compared with time-domain OCT, for example, StratusOCTTM (Carl Zeiss Meditec, CA, USA). With this benefit, this technique represents a powerful and ‘real-time’ tool that potentially can be used in the clinic to assist the diagnosis and management of glaucoma. The extremely high image-acquisition speed allows a 3D image to be yielded. Each image is obtained by an in-depth axial scan (A-scan), a cross-sectional 2D scan (B-scan) consisting of a series of consecutive A-scans and the 3D image volume formed by consecutive B-scans. However, despite this high performance, such an imaging technique can only be useful clinically if there is a quantitative method to provide numerical information to the clinician. Moreover, the greater acquisition speed of SD-OCT means that a much greater amount of data is generated, which undoubtedly poses a technical challenge to the computer-assisted analysis.

The majority of these studies [13

13. 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]

17

17. M. Mujat, R. Chan, B. Cense, B. Park, C. Joo, T. Akkin, T. Chen, and J. de Boer, “Retinal nerve fiber layer thickness map determined from optical coherence tomography images,” Opt. Express 13(23), 9480–9491 (2005). [CrossRef] [PubMed]

,23

23. 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). [CrossRef] [PubMed]

,24

24. 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). [CrossRef] [PubMed]

,26

26. 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). [CrossRef] [PubMed]

] filtered the images to remove distractive features such as the speckle noise. These filtering methods were controlled by subjectively selected parameters and had difficulties in ‘balancing’ the deduction of high speckle noise and preservation of structural edges, especially in images with low contrast. Haeker et al [19

19. 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]

] and Garvin et al [22

22. 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]

], on the other hand, proposed image averaging to create composite images from repeat scans. The composite images had higher signal-to-noise ratio, but multiple scans (six repeat scans in this case) were needed, which may exacerbate the detrimental effects of eye movement between the scans.

The aim of this study was to develop a new segmentation algorithm, FloatingCanvas, that has a balance between the robustness and efficiency. FloatingCanvas was implemented to quantify the retinal structures in 3D volume images around the optic nerve head (ONH) obtained with SD-OCT. It was used to process the whole image volume simultaneously and to reconstruct analytical surfaces for tissue layers or their boundaries. This method was designed to be robust to noise and artifact in volume images and thus required no pre-processing such as filtering or image averaging. It made use of the first and higher order gradient as the natural boundary between tissue layers. In this case, the algorithm searches for the retinal pigment epithelium (RPE) and retinal nerve fibre layer (RNFL) boundaries which consequently form the RNFLT measurement. Although RNFL and RPE are in theory the two tissue layers with the strongest reflectivity in these OCT images, they and their boundaries become less identifiable in images with overall or local artifacts. FloatingCanvas was tested on images taken from both healthy and glaucomatous subjects, and was compared with manual segmentation by the human expert. It was demonstrated that the algorithm was robust enough to detect the tissue layer boundaries in images with low contrast. The RNFLT maps obtained with this method were also compared with those derived from time-domain StratusOCTTM in healthy and glaucomatous subjects.

2. Methods

In this study, the SD-OCT images were acquired with the RTVue-100 (Optovue, Fremont, CA, USA) using the 4mm×4mm 3D volume scan protocol around the ONH with a depth of 2mm. This provides volumetric images with 101 B-scans comprised of 513 A-scans, each of 768 pixels in depth. Therefore, the distance between two B-scans is about 5 times that of two neighboring A-scans.

The axis in the image is subsequently denoted as x for the direction of the B-scan, y in the direction across all B-scans and z for the direction of A-scan, and the location of a pixel in the image Im is described by a vector x, y, zT or a two-tuples (x, z), where x is a column vector x, yT. The positive direction in an A-scan is defined to be from the top to the bottom of the image and will be described as ‘downward’ subsequently. Therefore, the value of the pixel in image Im is represented as Im(x, y, z) or Im(x, z). The pixel coordinates were all converted to a scale in microns.

2.1 Analytical surface modeled by Gaussian Process

FloatingCanvas searches for a tissue layer, or its boundaries, in the image by deforming a 3D analytical surface that is efficiently modeled by a Gaussian Process (GP) [29

29. C. K. I. Williams, “Regression with Gaussian Processes,” in Mathematics of Neural Networks: Models, Algorithms and Applications, (Kluwer, 1995).

]. The analytical surface is spanned by a sample of ‘skeleton’ points {(xiwi)}i=1N, where xi=<xi, yi>T is a column vector containing coordinates on the x- and y-axis for the ith ‘skeleton’ point, and wi is the coordinate on the z-axis. The skeleton points were evenly placed along the x- and y-axis to form a regular grid in the x-y space. The interval of the ‘skeleton’ point grid to model the anterior and posterior RNFL boundaries was chosen to be 100µm on both x- and y-axis, and the interval for RPE was 300µm given that RPE is expected to be smoother than RNFL boundaries around ONH. The GP model acts on these ‘skeleton’ points to determine a function f(x) that provides a calculation of the surface coordinate z on z-axis for any vector coordinate x on x- and y-axis.

Similar to a Gaussian distribution, the GP is defined by a mean function and a covariance function: f(x)~GP(m(x)k(xx*)), where the mean function m(x)=E(f(x)) is the expectation of f(x), and the covariance function is defined as the expectation: k(xx*)= E((f(x)m(x))(f(x*)m(x*))). In this case, the GP defines the joint probability between the skeleton points and the values f(X*) at arbitrary locations X* to be a Gaussian distribution:

[wf(X*)]~N(0, [K(XX)+δn2IK(XX*)K(X*X)K(X*X*)])
(1)

In Eq. (1), w is a column vector containing all {wi}i=1N and X is a matrix with {xi}i=1N in the columns, and a similar notation is used for X*. K(XX*) is a matrix with element Kij=k(xix*j), where xi and x*j are the ith and jth columns in X and X* respectively; k(xix*j) is defined to be an un-normalized Gaussian kernel function between data points xi and x*j: k(xix*j)=e12(((xi1x*j1))2+(xi2x*j2)2l2), where l is the length-scale of the Gaussian and is the main parameter to control the smoothness of the analytical surface, x1 and x2 represent the coordinates of point x on the x- and y-axis, respectively. A similar notation applies to K(XX), K(X*X) and K(X*X*). δn2 is the prior Gaussian noise variance of w and is fixed at 100µm in the algorithm. The parameter l was set to be 150µm for the surfaces modelling the anterior and posterior RNFL boundaries, and was 450µm for the surface to detect RPE.

Using the joint probability in Eq. (1), the conditional distribution p(f(X*)|XX*w) ~N(f¯(X*), cov(f¯(X*)) can be derived as [29

29. C. K. I. Williams, “Regression with Gaussian Processes,” in Mathematics of Neural Networks: Models, Algorithms and Applications, (Kluwer, 1995).

]:
f¯(X*)=K(X*X)(K(XX)+δn2I)1w
(2)
cov(f¯(X*))=K(X*X*)K(X*X)(K(XX)+δn2I)1K(XX*)
(3)
where I in Eq. (2) and (3) is an identity matrix with 1 on the diagonal and 0 off the diagonal.

If X* contains the coordinates of all points on the x- and y-axis, f¯(X*) returns the corresponding coordinates of the analytical surface on the z-axis.

2.2 Analytical surface deformation

Equation (4) describes a process, as indicated by the name of the algorithm, where the analytical surface acts as a ‘canvas’ floating in the 3D image and is driven by different forces: the analytical surface is initially ‘moved’ towards the target surface by a gravity force Fg; when the analytical surface is close to the target surface (i.e. the corresponding Θ function outputs 1), the force FImg takes over Fg and attaches the analytical surface on the target surface. The switch between Fg and FImg is controlled by the function Θ.

To form an equation about the parameter w, the left part of Eq. (4) is expanded by inserting Eq. (2) into Eq. (4) and applying the chain rule of derivative:

K(X*X)(K(XX)+δn2I)1dwdt=Θ¯Fg+ΘFImg
(5)

To form a concise notation, the matrix K(X*X)(K(XX)+δn2I)1 is substituted by Λ. Multiplying (ΛTΛ)1ΛTon both side of Eq. (5) gives:
dwdt=Λ(Θ¯Fg+ΘFImg)
(6)
where Λ is the pseudo-inverse [30

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

] of Λ and is given by (ΛTΛ)1ΛT. Λ acts as a projection matrix, which propagates the information from the pixels on the analytical surfaces to the skeleton points.

Equation (6) establishes the analytical surface deformation in FloatingCanvas: given the old parameter wold, the new wnew can be updated by:
wnew=wold+Λ(Θ¯oldFgold+ΘoldFImgold)Δt
(7)
according to the definition of the derivative in Eq. (6). The functions Fg, FImg and Θ are labeled with ‘old’ because the input f¯(X*) in these three functions is calculated using wold, and Δt is a sufficiently small time increment which is set to 0.1 in the algorithm.

The deformation in Eq. (7) is repeated, and in each iteration, the wold is substituted with wnew in last iteration. The algorithm stops when the value of w converges, or pragmatically when the change of w becomes sufficiently small (<0.26μm (0.1 pixel on z-axis) in the implementation).

2.3 Searching for tissue layers or their boundaries

Equation (8) acts as a ‘gravity’ pulling the analytical surface downwards from the top of the A-scan. The Θ function in Eq. (10) guaranties that this gravity function stops working when the analytical surface comes across a significant gradient that is larger than |g|. Equation (9) ‘attaches’ the surface to the local maximum of the image gradient magnitude when the function Θ outputs 1.

Similarly, the RPE layer is searched for by initializing the surface to be at the bottom of the A-scan, g=<0, 0, -10>T and the functions Fg, FImg and Θ are:
Fg(x*i, f¯(x*i))=|g|
(11)
Fimg(x*i, f¯(x*i))=Im(x*i, f¯(x*i))f¯(x*i)
(12)
Θ(x*i, f¯(x*i))={1Im(x*i, f¯(x*i))Immax(x*i)0otherwise
(13)
where Immax(x*i) is the maximum intensity of the pixels within a 150µm window below the surface point (x*i, f¯(x*i)).

The force in Eq. (11) pulls the surface upwards from the bottom of the A-scan when Θ in Eq. (13) shows that the intensity on the surface is larger than the current local maximum intensity Immax(x*i). Equation (12) ‘attaches’ the surface to the locations with the maximum local intensity when the function Θ outputs 1.

Θ(x*i, f¯(x*i))={1|Im(x*i, f¯(x*i))||g| and both constraints are met0otherwise
(14)

The g is set to be g=<0, 0, -15>T, and the functions Fg and FImg are the same with Eq. (8) and (9) if x*i is not in vessel region. Otherwise, Fg and FImg are set to 0. The vessel detection will be described in the subsequent section. The function Θ in Eq. (14) including the two constrains only brings the analytical surface near to the RNFL posterior boundary, which is eventually decided by the gradients in function FImg.

Benefiting from the segmentation in 3D space, the 4mm×4mm RNFLT map can simply be calculated as the difference between the segmented anterior and posterior RNFL, without smoothing out or interpolating the individually segmented B-scans [13

13. 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]

,14

14. 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]

,17

17. M. Mujat, R. Chan, B. Cense, B. Park, C. Joo, T. Akkin, T. Chen, and J. de Boer, “Retinal nerve fiber layer thickness map determined from optical coherence tomography images,” Opt. Express 13(23), 9480–9491 (2005). [CrossRef] [PubMed]

].

2.4 Vessel detection

There has been much recent discussion about how blood vessels influence current OCT segmentation algorithms causing bias in estimates of RNFLT [31

31. D. C. Hood, B. Fortune, S. N. Arthur, D. Xing, J. A. Salant, R. Ritch, and J. M. Liebmann, “Blood vessel contributions to retinal nerve fiber layer thickness profiles measured with optical coherence tomography,” J. Glaucoma 17(7), 519–528 (2008). [CrossRef] [PubMed]

,32

32. D. C. Hood, A. S. Raza, K. Y. Kay, S. F. Sandler, D. Xin, R. Ritch, and J. M. Liebmann, “A comparison of retinal nerve fiber layer (RNFL) thickness obtained with frequency and time domain optical coherence tomography (OCT),” Opt. Express 17(5), 3997–4003 (2009). [CrossRef] [PubMed]

]. In fact, RNFLT tends to be significantly overestimated or underestimated within the area of blood vessels. It is therefore necessary to mark and delineate as far as possible the blood vessels before detecting the RNFL posterior boundary.

FloatingCanvas identifies blood vessels by using the en-face image EF(x) obtained by averaging the 50 pixels below and above the analytical RPE [33

33. 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]

,34

34. T. Fabritius, S. Makita, Y. Hong, R. Myllylä, and Y. Yasuno, “Automated retinal shadow compensation of optical coherence tomography images,” J. Biomed. Opt. 14(1), 010503 (2009). [CrossRef] [PubMed]

] [Fig. 2(a)
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.
]. This detection scheme computes the ‘vesselness’ for each pixel in the en-face image. ‘Vesselness’ is a definition based on the analysis of eigenvalues of the Hessian matrix of image intensity [35

35. A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever, “Multiscale vessel enhancement filtering,” Medical Image Computing and Computer-Assisted Intervention 130–137 (1998).

]:
Svn={0                          vmax0e(νmin/νmax)20.52e12eνmin2+νmax2152      vmax>0
where νmin and νmax are the smaller and larger eigenvalues of a Hessian matrix which consists of the second-order derivatives of the en-face image EF(x):
[xi1xi1EF(xi)xi1xi2EF(xi)xi2xi1EF(xi)xi2xi2EF(xi)]
where, for example, xi1xi2EF(xi) is the second-order derivative of EF(xi) with respect to xi1 and then xi2. As it is shown in Fig. 2(b), the ‘vesselness’ score Svn provides a clear distinction between the vessel and non-vessel pixels, and even a simple thresholding (Svn>0.2) yields a satisfactory result. Eventually, the vessels are detected in the binary image as the area with more than 100 connected pixels.

2.5 Optic nerve head approximation

Physiologically, the RNFL near and within the ONH area changes direction and becomes the neural rim of the ONH. The idea of detecting the ONH area in this study is to exclude the ONH when displaying the RNFLT map. Therefore, the method described below was not designed to find an accurate contour line of the ONH, but to derive an approximated area of the ONH. The ONH is detected as the area bounded by the end-tips of the RPE.

The RPE tip is detected using the anterior RNFL and en-face image used in vessel detection. The detected anterior RNFL surface by FloatingCanvas is approximated by the combination of a quadratic and a Gaussian surface, which is a similar to a method proposed by Swindale et al when modelling scanning laser ophthalmoscope topography [36

36. 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]

]. The initial estimate of the ONH centre is set to be the centre of the fitted Gaussian component. The local intensity gradients at every pixel in the en-face image are then calculated in the x-y plane. The candidate pixels of RPE tips are required to meet three criteria. First, RPE tips should have a sufficiently large gradient (e.g. above the 75th percentile of the distribution of gradients). Second, considering the gradients of the intensity gradients at each pixel in the en-face image, the gradient of the intensity gradient of qualifying pixels is required to be near to 0. The gradient of the intensity gradient is near to 0 when the intensity gradient is at a local maximum. Third, there are two vectors of interest for each pixel in the en-face image: the local intensity gradient at the pixel (which is at right-angles to edges) and the vector connecting this pixel with the initial estimate of the ONH centre. It is required that the angle formed by these two vectors is smaller than 45° for the candidate RPE tip pixels. This criterion removes most pixels on vessel edges which also have strong gradient, because the angles between vectors for pixels on vessel edges are generally large (e.g. close to 90°). An ellipse is fit to the candidate RPE tip pixels using a Random Sample Consensus (RANSAC) parameter estimation (used by Li et al [37

37. D. Li, D. Winfield, and D. J. Parkhurst, “Starburst: A hybrid algorithm for video-based eye tracking combining feature-based and model-based approaches,” in IEEE Vision for Human-Computer Interaction Workshop at CVPR, 2005), 1–8.

] for ellipse detection in noisy data). A cubic spine is then fitted to the pixels that are close to the fitted ellipse to form the approximated contour of the RPE tips. The ONH centre is finally calculated as the geometric mean of the contour. The ONH area is removed when the RNFLT map is displayed (Fig. 4
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).
).

The algorithms in FloatingCanvas described above were implemented under Matlab (version 7.4.0 R2007a, The MathWorks, Inc., Natick, MA).

2.6 Validation

To validate the segmentation algorithm, 26 glaucomatous subjects (mean age of 52 (range 22 to 91) years) and 14 healthy subjects (mean age of 50 (range 16 to 67) years) were recruited. The study was approved by an ethics committee and informed consent, according to the tenets of the Declaration of Helsinki, was obtained prior to examination from each subject. Each subject was imaged 3 times with the RTVue-100 system 4mm×4mm 3D volume scan protocol. Images were acquired in both eyes of each subject during the same testing session by the same observer (PS) following the manufacturer instructions. Patient identifiers were removed from the data and the 3D volumes were transferred to a secure computer. FloatingCanvas was then applied to these 240 volume images to extract the RNFLT measurement.

The first validation compared the automated segmentation by FloatingCanvas with segmentation by human manual grading. One of the three repeat image volumes of a randomly chosen eye from each subject was randomly selected for manual segmentation. For each selected image volume, the 101 B-scans were evenly divided into 10 sections, each of which contains about 10 B-scans. One B-scan was then randomly chosen from each section for manual segmentation. With 40 subjects, 400 B-scans were manually delineated and the segmented surface positions were compared with those produced by FloatingCanvas. The mean and standard deviation (SD) of signed and absolute difference between the manual and FloatingCanvas segmentation was then evaluated for healthy and glaucomatous subjects respectively.

The second validation hypothesis was that, if the method is reliable, the estimated RNFLT from the FloatingCanvas segmentation would be equivalent across different width annuli around the ONH. Therefore, overall mean and quadrant mean RNFLT were estimated using two different calculation annuli: wide (0.58mm wide from the inner margin radius of 1.170mm) and narrow (0.29mm wide from the inner margin radius of 1.315mm) annuli (Fig. 3
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.
). The two annuli were centered on the same circle with a radius of 1.460mm, with one annulus twice as the width of the other.

Moreover, one of the most important parameters for the quantitative analysis of imaging techniques is the reproducibility, which directly relates to the reliability of the techniques and their ability to separate physiological changes from measurement variability and also to detect progressive RNFL loss over time. The reproducibility of RNFLT measurements was evaluated by estimating test-retest variability based on the three repeated measurements and the coefficient of variation (CV) for mean and quadrant RNFLT. We defined test-retest variability of RNFLT, expressed in micrometers, as twice the SD of the three repeated measurements. The coefficient of variation was calculated as the SD of the three measurements divided by the mean.

3. Results

FloatingCanvas segmented the retinal structures in all 240 SD-OCT volume scans without clinically spurious results. On average, it took 5.6±1.2 minutes to process a large image volume (513×101 A-scans) on one core of a Intel Core 2 Duo 2.4GHz CPU and 8GB RAM with single thread.

Figure 4 shows RNFLT maps from a healthy subject and a glaucomatous patient. The RNFLT map was colour-coded in micrometers with the ‘hotter’ colour denoting thicker RNFL. It should be noted that the healthy retina [Fig. 4(a)] has a much thicker RNFL in the superior and inferior quadrants compared with the nasal and temporal quadrants. This is consistent with the known normal retinal anatomy. The reduced RNFLT, especially in the superior and inferior quadrants, in the glaucomatous eye [Fig. 4(b)] can be observed and is consistent with clinical knowledge.

The mean and SD of the signed and absolute difference between the manual and FloatingCanvas segmentation for glaucomatous and healthy subjects are given in Table 1

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

table-icon
View This Table
| View All Tables
. For all manually segmented B-scans, the mean and SD of the absolute difference for all three boundaries are 4.3 ± 2.0µm. Therefore, on average, the FloatingCanvas segmentation differs from that of the human expert by 4.3µm, which is equivalent to 1.7 pixels on z-axis. The mean absolute difference between the manual and algorithm segmentation is relatively higher for glaucomatous retina compared with that of healthy retina but the difference is not statistically significant.RNFLT profile in the healthy and glaucomatous eyes is summarized in Table 2

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)

table-icon
View This Table
| View All Tables
.

There were no statistically significant differences between RNFLT measurements using the calculation annuli with different widths, which suggests that FloatingCanvas is robust and stable across the 3D volume. The quadrant RNFLT shows a difference between healthy and glaucomatous eyes. In general and on average, the healthy eyes, as expected, have a thicker RNFL, especially in the superior and inferior quadrants.

The reproducibility of the segmented RNFLT using SD-OCT was compared with the typical reproducibility of StratusOCT as reported in the literature using the standard scan protocol [38

38. D. L. Budenz, R. T. Chang, X. Huang, R. W. Knighton, and J. M. Tielsch, “Reproducibility of retinal nerve fiber thickness measurements using the stratus OCT in normal and glaucomatous eyes,” Invest. Ophthalmol. Vis. Sci. 46(7), 2440–2443 (2005). [CrossRef] [PubMed]

] in Table 3

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

table-icon
View This Table
| View All Tables
(healthy subjects) and Table 4

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

table-icon
View This Table
| View All Tables
(glaucomatous subjects). From Table 3 and Table 4, it can be seen that test-retest reproducibility in RNFLT measurements is better for both healthy and glaucomatous eyes with SD-OCT. RNFLT measurements were least reproducible in the nasal quadrant, with both SD-OCT and StratusOCT, while the segmented nasal measurement with SD-OCT showed markedly better reproducibility (~7µm vs 10.2µm in both normal and glaucomatous eyes). Moreover, RNFLT measurements in glaucomatous eyes were more variable than those of healthy eyes with both SD-OCT and StratusOCT, but SD-OCT showed much less variability and better reproducibility compared with StratusOCT, especially in the superior and inferior quadrants, which are the most important areas for glaucoma diagnosis. These results are consistent with the literatures about the reproducibility on another SD-OCT platform (Cirrus, Carl Zeiss Meditec, CA, USA) [39

39. J. S. Kim, H. Ishikawa, K. R. Sung, J. Xu, G. Wollstein, R. A. Bilonick, M. L. Gabriele, L. Kagemann, J. S. Duker, J. G. Fujimoto, and J. S. Schuman, “Retinal nerve fibre layer thickness measurement reproducibility improved with spectral domain optical coherence tomography,” Br. J. Ophthalmol. 93(8), 1057–1063 (2009). [CrossRef] [PubMed]

,40

40. J. C. Mwanza, R. T. Chang, D. L. Budenz, M. K. Durbin, M. G. Gendy, W. Shi, and W. J. Feuer, “Reproducibility of Peripapillary Retinal Nerve Fiber Layer Thickness and Optic Nerve Head Parameters Measured with CirrusTM HD-OCT in Glaucomatous Eyes,” Invest. Ophthalmol. Vis. Sci., iovs.10–5222 (2010).

].

4. Discussion

FloatingCanvas has been developed as an effective 3D segmentation method for SD-OCT volume scans centered on the ONH. It is important that automatic segmentation should be compared with the manual segmentation as the gold standard. The FloatingCanvas segmentation demonstrated good agreement with the human manual grading. It also provides a repeatable estimation of the RNFLT in the image volume. As opposed to the sparse area covered by the circular scans used in StratusOCT, the RNFLT maps cover a larger and clinically more useful area allowing for a more reliable measure of the RNFLT. The method has been tested on 240 3D volume scans acquired from both healthy and glaucomatous eyes of 40 subjects without spurious results under visual inspection. The results indicate that the RNFLT map gives a highly reproducible evaluation of a larger retina area compared with the last-generation time domain StratusOCT.

Overall, FloatingCanvas provides a robust and efficient delineation and evaluation of RNFL and RPE structures around the ONH. It can be a useful tool for clinically interpreting SD-OCT volumes for glaucoma diagnosis. The reproducible results can potentially be used for monitoring RNFLT changes in longitudinal studies. The larger scan area also improves the chance of achieving a stronger relationship of RNFLT measurements with visual function.

Acknowledgments

This research was supported in part by a research donation from Optovue, Fremont, CA, USA. Two of the authors (TH and DGH) have received a proportion of their funding from the Department of Health’s National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and the UCL Institute of Ophthalmology. The views expressed in this publication are those of the authors and not necessarily those of the Department of Health.

References and links

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]

2.

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]

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]

4.

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]

5.

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]

6.

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]

7.

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]

8.

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]

9.

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]

10.

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]

11.

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]

12.

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]

13.

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]

14.

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]

15.

C. Ahlers, C. Simader, W. Geitzenauer, G. Stock, P. Stetson, S. Dastmalchi, and U. Schmidt-Erfurth, “Automatic segmentation in three-dimensional analysis of fibrovascular pigmentepithelial detachment using high-definition optical coherence tomography,” Br. J. Ophthalmol. 92(2), 197–203 (2008). [CrossRef]

16.

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]

17.

M. Mujat, R. Chan, B. Cense, B. Park, C. Joo, T. Akkin, T. Chen, and J. de Boer, “Retinal nerve fiber layer thickness map determined from optical coherence tomography images,” Opt. Express 13(23), 9480–9491 (2005). [CrossRef] [PubMed]

18.

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]

19.

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]

20.

M. Niemeijer, M. Garvin, B. v. Ginneken, M. Sonka, and M. Abramoff, “Vessel segmentation in 3D spectral OCT scans of the retina,” Proc. SPIE, 6914, 69141R8 (2008).

21.

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]

22.

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]

23.

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). [CrossRef] [PubMed]

24.

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). [CrossRef] [PubMed]

25.

K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abramoff, “Segmentation of the optic disc in 3-D OCT scans of the optic nerve head,” IEEE Trans. Med. Imaging 29(1), 159–168 (2010). [CrossRef]

26.

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). [CrossRef] [PubMed]

27.

E. Götzinger, M. Pircher, W. Geitzenauer, C. Ahlers, B. Baumann, S. Michels, U. Schmidt-Erfurth, and C. K. Hitzenberger, “Retinal pigment epithelium segmentation by polarization sensitive optical coherence tomography,” Opt. Express 16(21), 16410–16422 (2008). [CrossRef] [PubMed]

28.

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]

29.

C. K. I. Williams, “Regression with Gaussian Processes,” in Mathematics of Neural Networks: Models, Algorithms and Applications, (Kluwer, 1995).

30.

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

31.

D. C. Hood, B. Fortune, S. N. Arthur, D. Xing, J. A. Salant, R. Ritch, and J. M. Liebmann, “Blood vessel contributions to retinal nerve fiber layer thickness profiles measured with optical coherence tomography,” J. Glaucoma 17(7), 519–528 (2008). [CrossRef] [PubMed]

32.

D. C. Hood, A. S. Raza, K. Y. Kay, S. F. Sandler, D. Xin, R. Ritch, and J. M. Liebmann, “A comparison of retinal nerve fiber layer (RNFL) thickness obtained with frequency and time domain optical coherence tomography (OCT),” Opt. Express 17(5), 3997–4003 (2009). [CrossRef] [PubMed]

33.

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]

34.

T. Fabritius, S. Makita, Y. Hong, R. Myllylä, and Y. Yasuno, “Automated retinal shadow compensation of optical coherence tomography images,” J. Biomed. Opt. 14(1), 010503 (2009). [CrossRef] [PubMed]

35.

A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever, “Multiscale vessel enhancement filtering,” Medical Image Computing and Computer-Assisted Intervention 130–137 (1998).

36.

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]

37.

D. Li, D. Winfield, and D. J. Parkhurst, “Starburst: A hybrid algorithm for video-based eye tracking combining feature-based and model-based approaches,” in IEEE Vision for Human-Computer Interaction Workshop at CVPR, 2005), 1–8.

38.

D. L. Budenz, R. T. Chang, X. Huang, R. W. Knighton, and J. M. Tielsch, “Reproducibility of retinal nerve fiber thickness measurements using the stratus OCT in normal and glaucomatous eyes,” Invest. Ophthalmol. Vis. Sci. 46(7), 2440–2443 (2005). [CrossRef] [PubMed]

39.

J. S. Kim, H. Ishikawa, K. R. Sung, J. Xu, G. Wollstein, R. A. Bilonick, M. L. Gabriele, L. Kagemann, J. S. Duker, J. G. Fujimoto, and J. S. Schuman, “Retinal nerve fibre layer thickness measurement reproducibility improved with spectral domain optical coherence tomography,” Br. J. Ophthalmol. 93(8), 1057–1063 (2009). [CrossRef] [PubMed]

40.

J. C. Mwanza, R. T. Chang, D. L. Budenz, M. K. Durbin, M. G. Gendy, W. Shi, and W. J. Feuer, “Reproducibility of Peripapillary Retinal Nerve Fiber Layer Thickness and Optic Nerve Head Parameters Measured with CirrusTM HD-OCT in Glaucomatous Eyes,” Invest. Ophthalmol. Vis. Sci., iovs.10–5222 (2010).

OCIS Codes
(100.0100) Image processing : Image processing
(170.4470) Medical optics and biotechnology : Ophthalmology
(170.4500) Medical optics and biotechnology : Optical coherence tomography
(170.4580) Medical optics and biotechnology : Optical diagnostics for medicine

ToC Category:
Medical Optics and Biotechnology

History
Original Manuscript: August 30, 2010
Revised Manuscript: October 7, 2010
Manuscript Accepted: October 10, 2010
Published: November 10, 2010

Citation
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)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-18-24-24595


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  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]
  2. 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]
  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]
  4. 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]
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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]
  11. 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]
  12. 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]
  13. 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]
  14. 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]
  15. C. Ahlers, C. Simader, W. Geitzenauer, G. Stock, P. Stetson, S. Dastmalchi, and U. Schmidt-Erfurth, “Automatic segmentation in three-dimensional analysis of fibrovascular pigmentepithelial detachment using high-definition optical coherence tomography,” Br. J. Ophthalmol. 92(2), 197–203 (2008). [CrossRef]
  16. 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]
  17. M. Mujat, R. Chan, B. Cense, B. Park, C. Joo, T. Akkin, T. Chen, and J. de Boer, “Retinal nerve fiber layer thickness map determined from optical coherence tomography images,” Opt. Express 13(23), 9480–9491 (2005). [CrossRef] [PubMed]
  18. 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]
  19. 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]
  20. M. Niemeijer, M. Garvin, B. v. Ginneken, M. Sonka, and M. Abramoff, “Vessel segmentation in 3D spectral OCT scans of the retina,” Proc. SPIE, 6914, 69141R8 (2008).
  21. 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]
  22. 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]
  23. 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). [CrossRef] [PubMed]
  24. 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). [CrossRef] [PubMed]
  25. K. Lee, M. Niemeijer, M. K. Garvin, Y. H. Kwon, M. Sonka, and M. D. Abramoff, “Segmentation of the optic disc in 3-D OCT scans of the optic nerve head,” IEEE Trans. Med. Imaging 29(1), 159–168 (2010). [CrossRef]
  26. 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). [CrossRef] [PubMed]
  27. E. Götzinger, M. Pircher, W. Geitzenauer, C. Ahlers, B. Baumann, S. Michels, U. Schmidt-Erfurth, and C. K. Hitzenberger, “Retinal pigment epithelium segmentation by polarization sensitive optical coherence tomography,” Opt. Express 16(21), 16410–16422 (2008). [CrossRef] [PubMed]
  28. 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]
  29. C. K. I. Williams, “Regression with Gaussian Processes,” in Mathematics of Neural Networks: Models, Algorithms and Applications, (Kluwer, 1995)
  30. G. Golub and W. Kahan, “Calculating the singular value and pseudo-inverse of a matrix,” SIAM Numerical Analysis 63, 205–224 (1965).
  31. D. C. Hood, B. Fortune, S. N. Arthur, D. Xing, J. A. Salant, R. Ritch, and J. M. Liebmann, “Blood vessel contributions to retinal nerve fiber layer thickness profiles measured with optical coherence tomography,” J. Glaucoma 17(7), 519–528 (2008). [CrossRef] [PubMed]
  32. D. C. Hood, A. S. Raza, K. Y. Kay, S. F. Sandler, D. Xin, R. Ritch, and J. M. Liebmann, “A comparison of retinal nerve fiber layer (RNFL) thickness obtained with frequency and time domain optical coherence tomography (OCT),” Opt. Express 17(5), 3997–4003 (2009). [CrossRef] [PubMed]
  33. 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]
  34. T. Fabritius, S. Makita, Y. Hong, R. Myllylä, and Y. Yasuno, “Automated retinal shadow compensation of optical coherence tomography images,” J. Biomed. Opt. 14(1), 010503 (2009). [CrossRef] [PubMed]
  35. A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever, “Multiscale vessel enhancement filtering,” Medical Image Computing and Computer-Assisted Intervention 130–137 (1998).
  36. 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]
  37. D. Li, D. Winfield, and D. J. Parkhurst, “Starburst: A hybrid algorithm for video-based eye tracking combining feature-based and model-based approaches,” in IEEE Vision for Human-Computer Interaction Workshop at CVPR, 2005), 1–8.
  38. D. L. Budenz, R. T. Chang, X. Huang, R. W. Knighton, and J. M. Tielsch, “Reproducibility of retinal nerve fiber thickness measurements using the stratus OCT in normal and glaucomatous eyes,” Invest. Ophthalmol. Vis. Sci. 46(7), 2440–2443 (2005). [CrossRef] [PubMed]
  39. J. S. Kim, H. Ishikawa, K. R. Sung, J. Xu, G. Wollstein, R. A. Bilonick, M. L. Gabriele, L. Kagemann, J. S. Duker, J. G. Fujimoto, and J. S. Schuman, “Retinal nerve fibre layer thickness measurement reproducibility improved with spectral domain optical coherence tomography,” Br. J. Ophthalmol. 93(8), 1057–1063 (2009). [CrossRef] [PubMed]
  40. J. C. Mwanza, R. T. Chang, D. L. Budenz, M. K. Durbin, M. G. Gendy, W. Shi, and W. J. Feuer, “Reproducibility of Peripapillary Retinal Nerve Fiber Layer Thickness and Optic Nerve Head Parameters Measured with CirrusTM HD-OCT in Glaucomatous Eyes,” Invest. Ophthalmol. Vis. Sci., iovs.10–5222 (2010).

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

Figures

Fig. 1 Fig. 2 Fig. 4
 
Fig. 3 Fig. 5
 

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited