Abstract

The correct segmentation of blood vessels in optical coherence tomography (OCT) images may be an important requirement for the analysis of intra-retinal layer thickness in human retinal diseases. We developed a shape model based procedure for the automatic segmentation of retinal blood vessels in spectral domain (SD)-OCT scans acquired with the Spectralis OCT system. The segmentation procedure is based on a statistical shape model that has been created through manual segmentation of vessels in a training phase. The actual segmentation procedure is performed after the approximate vessel position has been defined by a shadowgraph that assigns the lateral vessel positions. The active shape model method is subsequently used to segment blood vessel contours in axial direction. The automated segmentation results were validated against the manual segmentation of the same vessels by three expert readers. Manual and automated segmentations of 168 blood vessels from 34 B-scans were analyzed with respect to the deviations in the mean Euclidean distance and surface area. The mean Euclidean distance between the automatically and manually segmented contours (on average 4.0 pixels respectively 20 µm against all three experts) was within the range of the manually marked contours among the three readers (approximately 3.8 pixels respectively 18 µm for all experts). The area deviations between the automated and manual segmentation also lie within the range of the area deviations among the 3 clinical experts. Intra reader variability for the experts was between 0.9 and 0.94. We conclude that the automated segmentation approach is able to segment blood vessels with comparable accuracy as expert readers and will provide a useful tool in vessel analysis of whole C-scans, and in particular in multicenter trials.

© 2012 OSA

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References

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  1. W. Geitzenauer, C. K. Hitzenberger, and U. M. Schmidt-Erfurth, “Retinal optical coherence tomography: past, present and future perspectives,” Br. J. Ophthalmol.95(2), 171–177 (2011).
    [CrossRef] [PubMed]
  2. A. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Trans. Med. Imaging19(3), 203–210 (2000).
    [CrossRef] [PubMed]
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    [CrossRef] [PubMed]
  5. M. Niemeijer, M. K. Garvin, B. van Ginneken, M. Sonka, and M. D. Abràmoff, “Vessel segmentation in 3D spectral OCT scans of the retina,” Proc. SPIE6914, 69141R, 69141R-8 (2008).
    [CrossRef]
  6. J. Xu, D. Tolliver, H. Ishikawa, C. Wollstein, and J. Schuman, “Blood vessel segmentation with three-dimensional spectral domain optical coherence tomography,” International Patent no. WO/2010/138645 (Feb. 12, 2010).
  7. K. Lee, “Segmentations of the intraretinal surfaces, optic disc and retinal blood vessels in 3D-OCT scans,” Ph.D. dissertation (University of Iowa, 2009), pp. 57–69.
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    [CrossRef]
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2011

W. Geitzenauer, C. K. Hitzenberger, and U. M. Schmidt-Erfurth, “Retinal optical coherence tomography: past, present and future perspectives,” Br. J. Ophthalmol.95(2), 171–177 (2011).
[CrossRef] [PubMed]

2010

2008

M. Niemeijer, M. K. Garvin, B. van Ginneken, M. Sonka, and M. D. Abràmoff, “Vessel segmentation in 3D spectral OCT scans of the retina,” Proc. SPIE6914, 69141R, 69141R-8 (2008).
[CrossRef]

2007

2005

I. W. Selesnick, R. G. Baraniuk, and N. G. Kingsbury, “The dual-tree complex wavelet transform,” IEEE Signal Process. Mag.22(6), 123–151 (2005).
[CrossRef]

D. C. Fernández, “Delineating fluid-filled region boundaries in optical coherence tomography images of the retina,” IEEE Trans. Med. Imaging24(8), 929–945 (2005).
[CrossRef] [PubMed]

2004

H. Lamecker, M. Seebaß, H.-C. Hege, and P. Deuflhard, “A 3D statistical shape model of the pelvic bone for segmentation,” Proc. SPIE5370, 1341–1351 (2004).
[CrossRef]

2000

A. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Trans. Med. Imaging19(3), 203–210 (2000).
[CrossRef] [PubMed]

1999

C. Sinthanayothin, J. F. Boyce, H. L. Cook, and T. H. Williamson, “Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images,” Br. J. Ophthalmol.83(8), 902–910 (1999).
[CrossRef] [PubMed]

A. Kelemen, G. Székely, and G. Gerig, “Elastic model-based segmentation of 3-D neuroradiological data sets,” IEEE Trans. Med. Imaging18(10), 828–839 (1999).
[CrossRef] [PubMed]

1995

T. Cootes, C. Taylor, D. Cooper, and J. Graham, “Active shape models—their training and application,” Comput. Vis. Image Underst.61(1), 38–59 (1995).
[CrossRef]

1992

A. Hill and C. Taylor, “Model-based image interpretation using genetic algorithms,” Image Vis. Comput.10(5), 295–300 (1992).
[CrossRef]

1990

P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Trans. Pattern Anal. Mach. Intell.12(7), 629–639 (1990).
[CrossRef]

1986

J. M. Bland and D. G. Altman, “Statistical methods for assessing agreement between two methods of clinical measurement,” Lancet327(8476), 307–310 (1986).
[CrossRef] [PubMed]

Abràmoff, M. D.

M. Niemeijer, M. K. Garvin, B. van Ginneken, M. Sonka, and M. D. Abràmoff, “Vessel segmentation in 3D spectral OCT scans of the retina,” Proc. SPIE6914, 69141R, 69141R-8 (2008).
[CrossRef]

Altman, D. G.

J. M. Bland and D. G. Altman, “Statistical methods for assessing agreement between two methods of clinical measurement,” Lancet327(8476), 307–310 (1986).
[CrossRef] [PubMed]

Baraniuk, R. G.

I. W. Selesnick, R. G. Baraniuk, and N. G. Kingsbury, “The dual-tree complex wavelet transform,” IEEE Signal Process. Mag.22(6), 123–151 (2005).
[CrossRef]

Bizheva, K.

Bland, J. M.

J. M. Bland and D. G. Altman, “Statistical methods for assessing agreement between two methods of clinical measurement,” Lancet327(8476), 307–310 (1986).
[CrossRef] [PubMed]

Boyce, J. F.

C. Sinthanayothin, J. F. Boyce, H. L. Cook, and T. H. Williamson, “Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images,” Br. J. Ophthalmol.83(8), 902–910 (1999).
[CrossRef] [PubMed]

Clausi, D. A.

Cook, H. L.

C. Sinthanayothin, J. F. Boyce, H. L. Cook, and T. H. Williamson, “Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images,” Br. J. Ophthalmol.83(8), 902–910 (1999).
[CrossRef] [PubMed]

Cooper, D.

T. Cootes, C. Taylor, D. Cooper, and J. Graham, “Active shape models—their training and application,” Comput. Vis. Image Underst.61(1), 38–59 (1995).
[CrossRef]

Cootes, T.

T. Cootes, C. Taylor, D. Cooper, and J. Graham, “Active shape models—their training and application,” Comput. Vis. Image Underst.61(1), 38–59 (1995).
[CrossRef]

Deuflhard, P.

H. Lamecker, M. Seebaß, H.-C. Hege, and P. Deuflhard, “A 3D statistical shape model of the pelvic bone for segmentation,” Proc. SPIE5370, 1341–1351 (2004).
[CrossRef]

Drexler, W.

Fernández, D. C.

D. C. Fernández, “Delineating fluid-filled region boundaries in optical coherence tomography images of the retina,” IEEE Trans. Med. Imaging24(8), 929–945 (2005).
[CrossRef] [PubMed]

Garvin, M. K.

M. Niemeijer, M. K. Garvin, B. van Ginneken, M. Sonka, and M. D. Abràmoff, “Vessel segmentation in 3D spectral OCT scans of the retina,” Proc. SPIE6914, 69141R, 69141R-8 (2008).
[CrossRef]

Geitzenauer, W.

W. Geitzenauer, C. K. Hitzenberger, and U. M. Schmidt-Erfurth, “Retinal optical coherence tomography: past, present and future perspectives,” Br. J. Ophthalmol.95(2), 171–177 (2011).
[CrossRef] [PubMed]

Gerig, G.

A. Kelemen, G. Székely, and G. Gerig, “Elastic model-based segmentation of 3-D neuroradiological data sets,” IEEE Trans. Med. Imaging18(10), 828–839 (1999).
[CrossRef] [PubMed]

Goldbaum, M.

A. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Trans. Med. Imaging19(3), 203–210 (2000).
[CrossRef] [PubMed]

Graham, J.

T. Cootes, C. Taylor, D. Cooper, and J. Graham, “Active shape models—their training and application,” Comput. Vis. Image Underst.61(1), 38–59 (1995).
[CrossRef]

Gregori, G.

Hege, H.-C.

H. Lamecker, M. Seebaß, H.-C. Hege, and P. Deuflhard, “A 3D statistical shape model of the pelvic bone for segmentation,” Proc. SPIE5370, 1341–1351 (2004).
[CrossRef]

Hermann, B.

Hill, A.

A. Hill and C. Taylor, “Model-based image interpretation using genetic algorithms,” Image Vis. Comput.10(5), 295–300 (1992).
[CrossRef]

Hitzenberger, C. K.

W. Geitzenauer, C. K. Hitzenberger, and U. M. Schmidt-Erfurth, “Retinal optical coherence tomography: past, present and future perspectives,” Br. J. Ophthalmol.95(2), 171–177 (2011).
[CrossRef] [PubMed]

Hofer, B.

Hoover, A.

A. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Trans. Med. Imaging19(3), 203–210 (2000).
[CrossRef] [PubMed]

Jiao, S.

Kajic, V.

Kelemen, A.

A. Kelemen, G. Székely, and G. Gerig, “Elastic model-based segmentation of 3-D neuroradiological data sets,” IEEE Trans. Med. Imaging18(10), 828–839 (1999).
[CrossRef] [PubMed]

Kingsbury, N. G.

I. W. Selesnick, R. G. Baraniuk, and N. G. Kingsbury, “The dual-tree complex wavelet transform,” IEEE Signal Process. Mag.22(6), 123–151 (2005).
[CrossRef]

Kouznetsova, V.

A. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Trans. Med. Imaging19(3), 203–210 (2000).
[CrossRef] [PubMed]

Lamecker, H.

H. Lamecker, M. Seebaß, H.-C. Hege, and P. Deuflhard, “A 3D statistical shape model of the pelvic bone for segmentation,” Proc. SPIE5370, 1341–1351 (2004).
[CrossRef]

Malik, J.

P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Trans. Pattern Anal. Mach. Intell.12(7), 629–639 (1990).
[CrossRef]

Marshall, D.

Mishra, A.

Niemeijer, M.

M. Niemeijer, M. K. Garvin, B. van Ginneken, M. Sonka, and M. D. Abràmoff, “Vessel segmentation in 3D spectral OCT scans of the retina,” Proc. SPIE6914, 69141R, 69141R-8 (2008).
[CrossRef]

Perona, P.

P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Trans. Pattern Anal. Mach. Intell.12(7), 629–639 (1990).
[CrossRef]

Považay, B.

Puliafito, C. A.

Rosin, P. L.

Ruggeri, M.

Schmidt-Erfurth, U. M.

W. Geitzenauer, C. K. Hitzenberger, and U. M. Schmidt-Erfurth, “Retinal optical coherence tomography: past, present and future perspectives,” Br. J. Ophthalmol.95(2), 171–177 (2011).
[CrossRef] [PubMed]

Seebaß, M.

H. Lamecker, M. Seebaß, H.-C. Hege, and P. Deuflhard, “A 3D statistical shape model of the pelvic bone for segmentation,” Proc. SPIE5370, 1341–1351 (2004).
[CrossRef]

Selesnick, I. W.

I. W. Selesnick, R. G. Baraniuk, and N. G. Kingsbury, “The dual-tree complex wavelet transform,” IEEE Signal Process. Mag.22(6), 123–151 (2005).
[CrossRef]

Sinthanayothin, C.

C. Sinthanayothin, J. F. Boyce, H. L. Cook, and T. H. Williamson, “Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images,” Br. J. Ophthalmol.83(8), 902–910 (1999).
[CrossRef] [PubMed]

Sonka, M.

M. Niemeijer, M. K. Garvin, B. van Ginneken, M. Sonka, and M. D. Abràmoff, “Vessel segmentation in 3D spectral OCT scans of the retina,” Proc. SPIE6914, 69141R, 69141R-8 (2008).
[CrossRef]

Székely, G.

A. Kelemen, G. Székely, and G. Gerig, “Elastic model-based segmentation of 3-D neuroradiological data sets,” IEEE Trans. Med. Imaging18(10), 828–839 (1999).
[CrossRef] [PubMed]

Taylor, C.

T. Cootes, C. Taylor, D. Cooper, and J. Graham, “Active shape models—their training and application,” Comput. Vis. Image Underst.61(1), 38–59 (1995).
[CrossRef]

A. Hill and C. Taylor, “Model-based image interpretation using genetic algorithms,” Image Vis. Comput.10(5), 295–300 (1992).
[CrossRef]

van Ginneken, B.

M. Niemeijer, M. K. Garvin, B. van Ginneken, M. Sonka, and M. D. Abràmoff, “Vessel segmentation in 3D spectral OCT scans of the retina,” Proc. SPIE6914, 69141R, 69141R-8 (2008).
[CrossRef]

Wehbe, H.

Williamson, T. H.

C. Sinthanayothin, J. F. Boyce, H. L. Cook, and T. H. Williamson, “Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images,” Br. J. Ophthalmol.83(8), 902–910 (1999).
[CrossRef] [PubMed]

Wong, A.

Zhao, W.

Br. J. Ophthalmol.

C. Sinthanayothin, J. F. Boyce, H. L. Cook, and T. H. Williamson, “Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images,” Br. J. Ophthalmol.83(8), 902–910 (1999).
[CrossRef] [PubMed]

W. Geitzenauer, C. K. Hitzenberger, and U. M. Schmidt-Erfurth, “Retinal optical coherence tomography: past, present and future perspectives,” Br. J. Ophthalmol.95(2), 171–177 (2011).
[CrossRef] [PubMed]

Comput. Vis. Image Underst.

T. Cootes, C. Taylor, D. Cooper, and J. Graham, “Active shape models—their training and application,” Comput. Vis. Image Underst.61(1), 38–59 (1995).
[CrossRef]

IEEE Signal Process. Mag.

I. W. Selesnick, R. G. Baraniuk, and N. G. Kingsbury, “The dual-tree complex wavelet transform,” IEEE Signal Process. Mag.22(6), 123–151 (2005).
[CrossRef]

IEEE Trans. Med. Imaging

A. Kelemen, G. Székely, and G. Gerig, “Elastic model-based segmentation of 3-D neuroradiological data sets,” IEEE Trans. Med. Imaging18(10), 828–839 (1999).
[CrossRef] [PubMed]

A. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Trans. Med. Imaging19(3), 203–210 (2000).
[CrossRef] [PubMed]

D. C. Fernández, “Delineating fluid-filled region boundaries in optical coherence tomography images of the retina,” IEEE Trans. Med. Imaging24(8), 929–945 (2005).
[CrossRef] [PubMed]

IEEE Trans. Pattern Anal. Mach. Intell.

P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Trans. Pattern Anal. Mach. Intell.12(7), 629–639 (1990).
[CrossRef]

Image Vis. Comput.

A. Hill and C. Taylor, “Model-based image interpretation using genetic algorithms,” Image Vis. Comput.10(5), 295–300 (1992).
[CrossRef]

Lancet

J. M. Bland and D. G. Altman, “Statistical methods for assessing agreement between two methods of clinical measurement,” Lancet327(8476), 307–310 (1986).
[CrossRef] [PubMed]

Opt. Express

Proc. SPIE

H. Lamecker, M. Seebaß, H.-C. Hege, and P. Deuflhard, “A 3D statistical shape model of the pelvic bone for segmentation,” Proc. SPIE5370, 1341–1351 (2004).
[CrossRef]

M. Niemeijer, M. K. Garvin, B. van Ginneken, M. Sonka, and M. D. Abràmoff, “Vessel segmentation in 3D spectral OCT scans of the retina,” Proc. SPIE6914, 69141R, 69141R-8 (2008).
[CrossRef]

Other

J. Xu, D. Tolliver, H. Ishikawa, C. Wollstein, and J. Schuman, “Blood vessel segmentation with three-dimensional spectral domain optical coherence tomography,” International Patent no. WO/2010/138645 (Feb. 12, 2010).

K. Lee, “Segmentations of the intraretinal surfaces, optic disc and retinal blood vessels in 3D-OCT scans,” Ph.D. dissertation (University of Iowa, 2009), pp. 57–69.

A. Budai, G. Michelson, and J. Hornegger, “Multiscale Blood Vessel Segmentation in Retinal Fundus Images,” in Proceedings of Bildverarbeitung für die Medizin (Springer Verlag, 2010), pp. 261–265.

J. Hug, C. Brechbühler, and G. Székely, “Model-based Initialisation for Segmentation,” in Computer Vision—ECCV 2000 (Springer, 2000), pp. 290–306.

G. Edwards, T. Cootes, and C. Taylor, “Advances in active appearance models,” The Proceedings of the Seventh IEEE International Conference on Computer Vision (IEEE, 1999), Vol. 1, pp. 137–142.

R. Fisker, N. Schultz, N. Duta, and J. Carstensen, “A general scheme for training and optimization of the Grenander deformable template model,” in IEEE Conference on Computer Vision and Pattern Recognition,2000. Proceedings (IEEE, 2000), pp. 698–705.

M. Stegmann, R. Fisker, and B. Ersbøll, “Extending and applying active appearance models for automated, high precision segmentation in different image modalities,” in Scandinavian Conference on Image Analysis, (2001), pp. 90–97.

D. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning (Addison-Wesley, Bonn, 1989).

T. Cootes, C. Taylor, A. Lanitis, D. Cooper, and J. Graham, “Building and using flexible models incorporating grey-level information,” in Fourth International Conference on Computer Vision, 1993. Proceedings (1993), pp. 242–246.

H. Fleiss, Statistical Methods for Rates and Proportions, 2nd ed. (Wiley New York, 1981).

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Figures (8)

Fig. 1
Fig. 1

The segmentation algorithm consists of two parts. First, a statistical shape model is trained via the point distribution model in the solid procedure flow. The actual segmentation process of unseen images is then performed with the creation of a shadow graph and with the active shape model method based on the data of the statistical shape model from the first step (dotted arrows in the flow chart).

Fig. 2
Fig. 2

(a) Input B-scan recorded with the Spectralis OCT centered on a healthy human macula. (b) Processed speckle noise suppression with the Bayesian estimation approach. (c) Sampled A-scan of the raw image. The noise component is strong and blurs the original signal. (d) Sampled A-scan of the suppressed image. The noise component is much lower and the retinal structures are preserved.

Fig. 3
Fig. 3

On the left side, 20 shapes of different manually segmented blood vessels are shown. On the right side, the mean blood vessel shape is shown that has been obtained by averaging the 10 landmarks for all shapes of the set.

Fig. 4
Fig. 4

The grey-level variations are sampled on the normal of each landmark for all blood vessel shapes of the training set. The grey-level appearance is used to search for blood vessel objects in unseen images.

Fig. 5
Fig. 5

Segmentation procedure with the active shape model for one iteration. The mean shape model is placed near the image object (left) and the grey-level profiles of the image are compared to the model profiles for each landmark (middle). The model parameters are updated to move the model to the best positions marked as crosses (right).

Fig. 6
Fig. 6

(a) The basic concept of the blood vessel shadowgraph with the grey-level centers of each A-scan. If the grey-level intensities are distributed in the same manner for the top and bottom region of the A-scan, the center lies in the middle. If the intensities got weaker due to the blood vessel shadows, the center migrates upward. (b) The grey-level centers processed for an entire B-scan are visualized as green line and the lateral segmentation of the blood vessel shadow is marked by blue vertical lines after thresholding.

Fig. 7
Fig. 7

Segmentation results of the automated approach (blue lines) and the clinical expert 1 (green lines). (a), (b) and (c) references to the B-scans.

Fig. 8
Fig. 8

Area deviations between the algorithm and the three experts and the inter grader deviations are visualized as Bland-Altman plots.

Tables (3)

Tables Icon

Table 1 Mean Euclidian distance between the measured vessel contours for the entire set of 168 blood vessels

Tables Icon

Table 2 Contour diameters compared between the infrared fundus image and the OCT segmentations of Fig. 7

Tables Icon

Table 3 Area evaluation for the automated contours compared to the contours of the three experts for the entire set of 168 blood vessels

Equations (6)

Equations on this page are rendered with MathJax. Learn more.

x ¯ = 1 s t=1 s x t
d x t = x t x ¯
S= 1 s t=1 s d x t d x t T
P=[ p 1 p 2n ]
d ¯ (A,M)= 1 n m=1 n min a=1,...,j A(a)M(m) ,
d ¯ AM = d ¯ (A,M)+ d ¯ (M,A) 2

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