Abstract

Optical coherence tomography (OCT) of the macula has become increasingly important in the investigation of retinal pathology. However, deformable image registration, which is used for aligning subjects for pairwise comparisons, population averaging, and atlas label transfer, has not been well–developed and demonstrated on OCT images. In this paper, we present a deformable image registration approach designed specifically for macular OCT images. The approach begins with an initial translation to align the fovea of each subject, followed by a linear rescaling to align the top and bottom retinal boundaries. Finally, the layers within the retina are aligned by a deformable registration using one-dimensional radial basis functions. The algorithm was validated using manual delineations of retinal layers in OCT images from a cohort consisting of healthy controls and patients diagnosed with multiple sclerosis (MS). We show that the algorithm overcomes the shortcomings of existing generic registration methods, which cannot be readily applied to OCT images. A successful deformable image registration algorithm for macular OCT opens up a variety of population based analysis techniques that are regularly used in other imaging modalities, such as spatial normalization, statistical atlas creation, and voxel based morphometry. Examples of these applications are provided to demonstrate the potential benefits such techniques can have on our understanding of retinal disease. In particular, included is a pilot study of localized volumetric changes between healthy controls and MS patients using the proposed registration algorithm.

© 2014 Optical Society of America

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2013 (5)

J. N. Ratchford, S. Saidha, E. S. Sotirchos, J. A. Oh, M. A. Seigo, C. Eckstein, M. K. Durbin, J. D. Oakley, S. A. Meyer, A. Conger, T. C. Frohman, S. D. Newsome, L. J. Balcer, E. M. Frohman, and P. A. Calabresi, “Active MS is associated with accelerated retinal ganglion cell/inner plexiform layer thinning,” Neurology80, 47–54 (2013).
[CrossRef]

S. Saidha, E. S. Sotirchos, J. Oh, S. B. Syc, M. A. Seigo, N. Shiee, C. Eckstein, M. K. Durbin, J. D. Oakley, S. A. Meyer, T. C. Frohman, S. Newsome, J. N. Ratchford, L. J. Balcer, D. L. Pham, C. M. Crainiceanu, E. M. Frohman, D. S. Reich, and P. A. Calabresi, “Relationships between retinal axonal and neuronal measures and global central nervous system pathology in Multiple Sclerosis,” JAMA Neurology70, 34–43 (2013).
[CrossRef] [PubMed]

A. Sotiras, C. Davatzikos, and N. Paragios, “Deformable medical image registration: A survey,” IEEE Trans. Med. Imag.32, 1153–1190 (2013).
[CrossRef]

A. N. Kuo, R. P. McNabb, S. J. Chiu, M. A. El-Dairi, S. Farsiu, C. A. Toth, and J. A. Izatt, “Correction of ocular shape in retinal optical coherence tomography and effect on current clinical measures,” Am. J. Ophthalmol.156, 304–311 (2013).
[CrossRef] [PubMed]

A. Lang, A. Carass, M. Hauser, E. S. Sotirchos, P. A. Calabresi, H. S. Ying, and J. L. Prince, “Retinal layer segmentation of macular OCT images using boundary classification,” Biomed. Opt. Express4, 1133–1152 (2013).
[CrossRef] [PubMed]

2012 (6)

Y. M. Liew, R. A. McLaughlin, F. M. Wood, and D. D. Sampson, “Motion correction of in vivo three-dimensional optical coherence tomography of human skin using a fiducial marker,” Biomed. Opt. Express3, 1774 (2012).
[CrossRef] [PubMed]

A. Giani, M. Pellegrini, A. Invernizzi, M. Cigada, and G. Staurenghi, “Aligning scan locations from consecutive spectral-domain optical coherence tomography examinations: A comparison among different strategies,” Invest. Ophthalmol. Vis. Sci.53, 7637–7643 (2012).
[CrossRef] [PubMed]

J. Xu, H. Ishikawa, G. Wollstein, L. Kagemann, and J. S. Schuman, “Alignment of 3-d optical coherence tomography scans to correct eye movement using a particle filtering,” IEEE Trans. Med. Imag.31, 1337–1345 (2012).
[CrossRef]

S. Saidha, E. S. Sotirchos, M. A. Ibrahim, C. M. Crainiceanu, J. M. Gelfand, Y. J. Sepah, J. N. Ratchford, J. Oh, M. A. Seigo, S. D. Newsome, L. J. Balcer, E. M. Frohman, A. J. Green, Q. D. Nguyen, and P. A. Calabresi, “Microcystic macular oedema, thickness of the inner nuclear layer of the retina, and disease characteristics in multiple sclerosis: A retrospective study,” The Lancet Neurology11, 963–972 (2012).
[CrossRef]

P. A. Keane, P. J. Patel, S. Liakopoulos, F. M. Heussen, S. R. Sadda, and A. Tufail, “Evaluation of age-related macular degeneration with optical coherence tomography,” Surv. Ophthalmol.57, 389–414 (2012).
[CrossRef] [PubMed]

G. Querques, R. Lattanzio, L. Querques, C. Del Turco, R. Forte, L. Pierro, E. H. Souied, and F. Bandello, “Enhanced depth imaging optical coherence tomography in Type 2 diabetes,” Invest. Ophthalmol. Vis. Sci.53, 6017–6024 (2012).
[CrossRef] [PubMed]

2011 (6)

B. B. Avants, N. J. Tustison, G. Song, P. A. Cook, A. Klein, and J. C. Gee, “A reproducible evaluation of ANTs similarity metric performance in brain image registration,” NeuroImage54, 2033–2044 (2011).
[CrossRef]

Y. Ou, A. Sotiras, N. Paragios, and C. Davatzikos, “Dramms: Deformable registration via attribute matching and mutual-saliency weighting,” Med. Image Anal.15, 622–639 (2011).
[CrossRef]

W. Bai and M. Brady, “Motion correction and attenuation correction for respiratory gated PET images,” IEEE Trans. Med. Imag.30, 351–365 (2011).
[CrossRef]

S. Khullar, A. M. Michael, N. D. Cahill, K. A. Kiehl, G. Pearlson, S. A. Baum, and V. D. Calhoun, “ICA-fNORM: Spatial normalization of fMRI data using intrinsic group-ICA networks,” Front. Syst. Neurosci593(2011).
[CrossRef] [PubMed]

B. J. Lujan, A. Roorda, R. W. Knighton, and J. Carroll, “Revealing henle’s fiber layer using spectral domain optical coherence tomography,” Invest. Ophthalmol. Visual Sci.52, 1486–1492 (2011).
[CrossRef]

B. Antony, M. D. Abràmoff, L. Tang, W. D. Ramdas, J. R. Vingerling, N. M. Jansonius, K. Lee, Y. H. Kwon, M. Sonka, and M. K. Garvin, “Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images,” Biomed. Opt. Express2, 2403–2416 (2011).
[CrossRef] [PubMed]

2010 (7)

E. Gibson, M. Young, M. V. Sarunic, and M. F. Beg, “Optic nerve head registration via hemispherical surface and volume registration,” IEEE Trans. Biomed. Eng.57, 2592–2595 (2010).
[CrossRef] [PubMed]

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. Express18, 19413–19428 (2010).
[CrossRef] [PubMed]

B. C. Lucas, J. A. Bogovic, A. Carass, P.-L. Bazin, J. L. Prince, D. L. Pham, and B. A. Landman, “The Java Image Science Toolkit (JIST) for rapid prototyping and publishing of neuroimaging software,” Neuroinformatics8, 5–17 (2010).
[CrossRef] [PubMed]

A. J. Green, S. McQuaid, S. L. Hauser, I. V. Allen, and R. Lyness, “Ocular pathology in multiple sclerosis: retinal atrophy and inflammation irrespective of disease duration,” Brain133, 1591–1601 (2010).
[CrossRef] [PubMed]

A. Klein, S. S. Ghosh, B. Avants, B. T. T. Yeo, B. Fischl, B. Ardekani, J. C. Gee, J. J. Mann, and R. V. Parsey, “Evaluation of volume-based and surface-based brain image registration methods,” NeuroImage51, 214–220 (2010).
[CrossRef] [PubMed]

S. Gerber, T. Tasdizen, P. T. Fletcher, S. Joshi, and R. Whitaker, “Manifold modeling for brain population analysis,” Med. Image Anal.14, 643–653 (2010).
[CrossRef] [PubMed]

Y. Lu, Z. Li, X. Zhang, B. Ming, J. Jia, R. Wang, and D. Ma, “Retinal nerve fiber layer structure abnormalities in early Alzheimer’s disease: Evidence in optical coherence tomography,” Neurosci. Lett.480, 69–72 (2010).
[CrossRef] [PubMed]

2009 (3)

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

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. Imag.28, 1436–1447 (2009).
[CrossRef]

J. G. Fujimoto, W. Drexler, J. S. Schuman, and C. K. Hitzenberger, “Optical coherence tomography (OCT) in ophthalmology: Introduction,” Opt. Express17, 3978–3979 (2009).
[CrossRef] [PubMed]

2008 (3)

B. B. Avants, C. L. Epstein, M. Grossman, and J. C. Gee, “Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain,” Med. Image Anal.12, 26–41 (2008).
[CrossRef]

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

Y. Fan, S. M. Resnick, X. Wu, and C. Davatzikos, “Structural and functional biomarkers of prodromal Alzheimer’s disease: A high-dimensional pattern classification study,” NeuroImage41, 277–285 (2008).
[CrossRef] [PubMed]

2007 (1)

T. M. Jørgensen, J. Thomadsen, U. Christensen, W. Soliman, and B. Sander, “Enhancing the signal-to-noise ratio in ophthalmic optical coherence tomography by image registration—method and clinical examples,” J. Biomed. Opt.12, 041208 (2007).
[CrossRef]

2005 (3)

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, 2012–2017 (2005).
[CrossRef] [PubMed]

M. Auer, P. Regitnig, and G. A. Holzapfel, “An automatic nonrigid registration for stained histological sections,” IEEE Trans. Imag. Proc.14, 475–486 (2005).
[CrossRef]

K. K. Brock, M. B. Sharpe, L. A. Dawson, S. M. Kim, and D. A. Jaffray, “Accuracy of finite element model-based multi-organ deformable image registration,” Med. Phys.32, 1647–1659 (2005).
[CrossRef] [PubMed]

2004 (1)

B. Avants and J. C. Gee, “Geodesic estimation for large deformation anatomical shape averaging and interpolation,” NeuroImage23, S139–S150 (2004).
[CrossRef] [PubMed]

2003 (4)

D. Rueckert, A. F. Frangi, and J. A. Schnabel, “Automatic construction of 3-d statistical deformation models of the brain using nonrigid registration,” IEEE Trans. Med. Imag.22, 1014–1025 (2003).
[CrossRef]

G. K. Rohde, A. Aldroubi, and B. M. Dawant, “The adaptive bases algorithm for intensity based nonrigid image registration,” IEEE Trans. Med. Imag.22, 1470–1479 (2003).
[CrossRef]

V. Guedes, J. S. Schuman, E. Hertzmark, G. Wollstein, A. Correnti, R. Mancini, D. Lederer, S. Voskanian, L. Velazquez, H. M. Pakter, T. Pedut-Kloizman, J. G. Fujimoto, and C. Mattox, “Optical coherence tomography measurement of macular and nerve fiber layer thickness in normal and glaucomatous human eyes,” Ophthalmology110, 177–189 (2003).
[CrossRef] [PubMed]

S. M. Resnick, D. L. Pham, M. A. Kraut, A. Zonderman, and C. Davatzikos, “Longitudinal magnetic resonance imaging studies of older adults: A shrinking brain,” J. Neurosci.23, 3295–3301 (2003).
[PubMed]

2001 (3)

C. Davatzikos, A. Genc, D. Xu, and S. M. Resnick, “Voxel-based morphometry using the RAVENS maps: Methods and validation using simulated longitudinal atrophy,” NeuroImage14, 1361–1369 (2001).
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C. D. Good, I. S. Johnsrude, J. Ashburner, R. N. A. Henson, K. J. Friston, and R. S. J. Frackowiak, “A voxel-based morphometric study of ageing in 465 normal adult human brains,” NeuroImage14, 21–36 (2001).
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D. Koozekanani, K. Boyer, and C. Roberts, “Retinal thickness measurements from optical coherence tomography using a Markov boundary model,” IEEE Trans. Med. Imag.20, 900–916 (2001).
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2000 (3)

J. Ashburner and K. J. Friston, “Voxel-based morphometry—the methods,” NeuroImage11, 805821 (2000).
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A. Guimond, J. Meunier, and J.-P. Thirion, “Average brain models: A convergence study,” Computer vision and image understanding77, 192–210 (2000).
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1998 (1)

A. F. Goldszal, C. Davatzikos, D. Pham, M. X. H. Yan, R. N. Bryan, and S. M. Resnick, “An image-processing system for qualitative and quantitative volumetric analysis of brain images,” J. Computer Assisted Tomography22, 827–837 (1998).
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1994 (1)

J. B. Kerrison, T. Flynn, and W. R. Green, “Retinal pathologic changes in multiple sclerosis,” Retina14, 445–451 (1994).
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1993 (1)

M. I. Miller, G. E. Christensen, Y. Amit, and U. Grenander, “Mathematical textbook of deformable neuroanatomies,” Proc. Natl. Acad. Sci.90, 11944–11948 (1993).
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1945 (1)

L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology26, 297–302 (1945).
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Abràmoff, M. D.

B. Antony, M. D. Abràmoff, L. Tang, W. D. Ramdas, J. R. Vingerling, N. M. Jansonius, K. Lee, Y. H. Kwon, M. Sonka, and M. K. Garvin, “Automated 3-D method for the correction of axial artifacts in spectral-domain optical coherence tomography images,” Biomed. Opt. Express2, 2403–2416 (2011).
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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. Imag.28, 1436–1447 (2009).
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M. Niemeijer, K. Lee, M. K. Garvin, M. D. Abràmoff, and M. Sonka, “Registration of 3D spectral OCT volumes combining ICP with a graph-based approach,” in “Proc. SPIE-MI 2012,” (San Diego, CA, 2012).

M. Niemeijer, M. K. Garvin, K. Lee, B. van Ginneken, M. D. Abràmoff, and M. Sonka, “Registration of 3D spectral OCT volumes using 3D SIFT feature point matching,” in “Proc. SPIE-MI 2009,” (Lake Buena Vista, FL, 2009).

Aldroubi, A.

G. K. Rohde, A. Aldroubi, and B. M. Dawant, “The adaptive bases algorithm for intensity based nonrigid image registration,” IEEE Trans. Med. Imag.22, 1470–1479 (2003).
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Allen, I. V.

A. J. Green, S. McQuaid, S. L. Hauser, I. V. Allen, and R. Lyness, “Ocular pathology in multiple sclerosis: retinal atrophy and inflammation irrespective of disease duration,” Brain133, 1591–1601 (2010).
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Amit, Y.

M. I. Miller, G. E. Christensen, Y. Amit, and U. Grenander, “Mathematical textbook of deformable neuroanatomies,” Proc. Natl. Acad. Sci.90, 11944–11948 (1993).
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Antony, B.

Ardekani, B.

A. Klein, S. S. Ghosh, B. Avants, B. T. T. Yeo, B. Fischl, B. Ardekani, J. C. Gee, J. J. Mann, and R. V. Parsey, “Evaluation of volume-based and surface-based brain image registration methods,” NeuroImage51, 214–220 (2010).
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Ashburner, J.

C. D. Good, I. S. Johnsrude, J. Ashburner, R. N. A. Henson, K. J. Friston, and R. S. J. Frackowiak, “A voxel-based morphometric study of ageing in 465 normal adult human brains,” NeuroImage14, 21–36 (2001).
[CrossRef] [PubMed]

E. A. Maguire, D. G. Gadian, I. S. Johnsrude, C. D. Good, J. Ashburner, R. S. J. Frackowiak, and C. D. Frith, “Navigation-related structural change in the hippocampi of taxi drivers,” Proc. Nat. Acad. Sci.97, 4398–4403 (2000).
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J. Ashburner and K. J. Friston, “Voxel-based morphometry—the methods,” NeuroImage11, 805821 (2000).
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Auer, M.

M. Auer, P. Regitnig, and G. A. Holzapfel, “An automatic nonrigid registration for stained histological sections,” IEEE Trans. Imag. Proc.14, 475–486 (2005).
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Avants, B.

A. Klein, S. S. Ghosh, B. Avants, B. T. T. Yeo, B. Fischl, B. Ardekani, J. C. Gee, J. J. Mann, and R. V. Parsey, “Evaluation of volume-based and surface-based brain image registration methods,” NeuroImage51, 214–220 (2010).
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B. Avants and J. C. Gee, “Geodesic estimation for large deformation anatomical shape averaging and interpolation,” NeuroImage23, S139–S150 (2004).
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Avants, B. B.

B. B. Avants, N. J. Tustison, G. Song, P. A. Cook, A. Klein, and J. C. Gee, “A reproducible evaluation of ANTs similarity metric performance in brain image registration,” NeuroImage54, 2033–2044 (2011).
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B. B. Avants, C. L. Epstein, M. Grossman, and J. C. Gee, “Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain,” Med. Image Anal.12, 26–41 (2008).
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Bai, W.

W. Bai and M. Brady, “Motion correction and attenuation correction for respiratory gated PET images,” IEEE Trans. Med. Imag.30, 351–365 (2011).
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Balcer, L. J.

J. N. Ratchford, S. Saidha, E. S. Sotirchos, J. A. Oh, M. A. Seigo, C. Eckstein, M. K. Durbin, J. D. Oakley, S. A. Meyer, A. Conger, T. C. Frohman, S. D. Newsome, L. J. Balcer, E. M. Frohman, and P. A. Calabresi, “Active MS is associated with accelerated retinal ganglion cell/inner plexiform layer thinning,” Neurology80, 47–54 (2013).
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S. Saidha, E. S. Sotirchos, J. Oh, S. B. Syc, M. A. Seigo, N. Shiee, C. Eckstein, M. K. Durbin, J. D. Oakley, S. A. Meyer, T. C. Frohman, S. Newsome, J. N. Ratchford, L. J. Balcer, D. L. Pham, C. M. Crainiceanu, E. M. Frohman, D. S. Reich, and P. A. Calabresi, “Relationships between retinal axonal and neuronal measures and global central nervous system pathology in Multiple Sclerosis,” JAMA Neurology70, 34–43 (2013).
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S. Saidha, E. S. Sotirchos, M. A. Ibrahim, C. M. Crainiceanu, J. M. Gelfand, Y. J. Sepah, J. N. Ratchford, J. Oh, M. A. Seigo, S. D. Newsome, L. J. Balcer, E. M. Frohman, A. J. Green, Q. D. Nguyen, and P. A. Calabresi, “Microcystic macular oedema, thickness of the inner nuclear layer of the retina, and disease characteristics in multiple sclerosis: A retrospective study,” The Lancet Neurology11, 963–972 (2012).
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E. M. Frohman, J. G. Fujimoto, T. C. Frohman, P. A. Calabresi, G. Cutter, and L. J. Balcer, “Optical coherence tomography: A window into the mechanisms of multiple sclerosis,” Nat. Clin. Pract. Neuro.4, 664–675 (2008).
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Bandello, F.

G. Querques, R. Lattanzio, L. Querques, C. Del Turco, R. Forte, L. Pierro, E. H. Souied, and F. Bandello, “Enhanced depth imaging optical coherence tomography in Type 2 diabetes,” Invest. Ophthalmol. Vis. Sci.53, 6017–6024 (2012).
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Baum, S. A.

S. Khullar, A. M. Michael, N. D. Cahill, K. A. Kiehl, G. Pearlson, S. A. Baum, and V. D. Calhoun, “ICA-fNORM: Spatial normalization of fMRI data using intrinsic group-ICA networks,” Front. Syst. Neurosci593(2011).
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Bazin, P.-L.

B. C. Lucas, J. A. Bogovic, A. Carass, P.-L. Bazin, J. L. Prince, D. L. Pham, and B. A. Landman, “The Java Image Science Toolkit (JIST) for rapid prototyping and publishing of neuroimaging software,” Neuroinformatics8, 5–17 (2010).
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Beaton, S.

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

E. Gibson, M. Young, M. V. Sarunic, and M. F. Beg, “Optic nerve head registration via hemispherical surface and volume registration,” IEEE Trans. Biomed. Eng.57, 2592–2595 (2010).
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Bodis-Wollner, I. G.

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

B. C. Lucas, J. A. Bogovic, A. Carass, P.-L. Bazin, J. L. Prince, D. L. Pham, and B. A. Landman, “The Java Image Science Toolkit (JIST) for rapid prototyping and publishing of neuroimaging software,” Neuroinformatics8, 5–17 (2010).
[CrossRef] [PubMed]

Boyer, K.

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

Brady, M.

W. Bai and M. Brady, “Motion correction and attenuation correction for respiratory gated PET images,” IEEE Trans. Med. Imag.30, 351–365 (2011).
[CrossRef]

Brock, K. K.

K. K. Brock, M. B. Sharpe, L. A. Dawson, S. M. Kim, and D. A. Jaffray, “Accuracy of finite element model-based multi-organ deformable image registration,” Med. Phys.32, 1647–1659 (2005).
[CrossRef] [PubMed]

Bryan, R. N.

A. F. Goldszal, C. Davatzikos, D. Pham, M. X. H. Yan, R. N. Bryan, and S. M. Resnick, “An image-processing system for qualitative and quantitative volumetric analysis of brain images,” J. Computer Assisted Tomography22, 827–837 (1998).
[CrossRef]

Burns, T. L.

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. Imag.28, 1436–1447 (2009).
[CrossRef]

Cahill, N. D.

S. Khullar, A. M. Michael, N. D. Cahill, K. A. Kiehl, G. Pearlson, S. A. Baum, and V. D. Calhoun, “ICA-fNORM: Spatial normalization of fMRI data using intrinsic group-ICA networks,” Front. Syst. Neurosci593(2011).
[CrossRef] [PubMed]

Calabresi, P.

M. Chen, A. Carass, D. Reich, P. Calabresi, D. Pham, and J. Prince, “Voxel-wise displacement as independent features in classification of multiple sclerosis,” in “Proc. SPIE-MI 2013,” (Lake Buena Vista, FL, 2013).

Calabresi, P. A.

S. Saidha, E. S. Sotirchos, J. Oh, S. B. Syc, M. A. Seigo, N. Shiee, C. Eckstein, M. K. Durbin, J. D. Oakley, S. A. Meyer, T. C. Frohman, S. Newsome, J. N. Ratchford, L. J. Balcer, D. L. Pham, C. M. Crainiceanu, E. M. Frohman, D. S. Reich, and P. A. Calabresi, “Relationships between retinal axonal and neuronal measures and global central nervous system pathology in Multiple Sclerosis,” JAMA Neurology70, 34–43 (2013).
[CrossRef] [PubMed]

A. Lang, A. Carass, M. Hauser, E. S. Sotirchos, P. A. Calabresi, H. S. Ying, and J. L. Prince, “Retinal layer segmentation of macular OCT images using boundary classification,” Biomed. Opt. Express4, 1133–1152 (2013).
[CrossRef] [PubMed]

J. N. Ratchford, S. Saidha, E. S. Sotirchos, J. A. Oh, M. A. Seigo, C. Eckstein, M. K. Durbin, J. D. Oakley, S. A. Meyer, A. Conger, T. C. Frohman, S. D. Newsome, L. J. Balcer, E. M. Frohman, and P. A. Calabresi, “Active MS is associated with accelerated retinal ganglion cell/inner plexiform layer thinning,” Neurology80, 47–54 (2013).
[CrossRef]

S. Saidha, E. S. Sotirchos, M. A. Ibrahim, C. M. Crainiceanu, J. M. Gelfand, Y. J. Sepah, J. N. Ratchford, J. Oh, M. A. Seigo, S. D. Newsome, L. J. Balcer, E. M. Frohman, A. J. Green, Q. D. Nguyen, and P. A. Calabresi, “Microcystic macular oedema, thickness of the inner nuclear layer of the retina, and disease characteristics in multiple sclerosis: A retrospective study,” The Lancet Neurology11, 963–972 (2012).
[CrossRef]

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

M. Chen, A. Lang, E. Sotirchos, H. S. Ying, P. A. Calabresi, J. L. Prince, and A. Carass, “Deformable registration of macular oct using a-mode scan similarity,” in “Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on,” (IEEE, 2013), pp. 476–479.

Calhoun, V. D.

S. Khullar, A. M. Michael, N. D. Cahill, K. A. Kiehl, G. Pearlson, S. A. Baum, and V. D. Calhoun, “ICA-fNORM: Spatial normalization of fMRI data using intrinsic group-ICA networks,” Front. Syst. Neurosci593(2011).
[CrossRef] [PubMed]

Carass, A.

A. Lang, A. Carass, M. Hauser, E. S. Sotirchos, P. A. Calabresi, H. S. Ying, and J. L. Prince, “Retinal layer segmentation of macular OCT images using boundary classification,” Biomed. Opt. Express4, 1133–1152 (2013).
[CrossRef] [PubMed]

B. C. Lucas, J. A. Bogovic, A. Carass, P.-L. Bazin, J. L. Prince, D. L. Pham, and B. A. Landman, “The Java Image Science Toolkit (JIST) for rapid prototyping and publishing of neuroimaging software,” Neuroinformatics8, 5–17 (2010).
[CrossRef] [PubMed]

M. Chen, A. Carass, D. Reich, P. Calabresi, D. Pham, and J. Prince, “Voxel-wise displacement as independent features in classification of multiple sclerosis,” in “Proc. SPIE-MI 2013,” (Lake Buena Vista, FL, 2013).

M. Chen, A. Lang, E. Sotirchos, H. S. Ying, P. A. Calabresi, J. L. Prince, and A. Carass, “Deformable registration of macular oct using a-mode scan similarity,” in “Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on,” (IEEE, 2013), pp. 476–479.

A. Lang, A. Carass, E. Sotirchos, and J. L. Prince, “Segmentation of retinal OCT images using a random forest classifier,” in “Proc. SPIE-MI 2013,” (Lake Buena Vista, FL, 2013).

Carroll, J.

B. J. Lujan, A. Roorda, R. W. Knighton, and J. Carroll, “Revealing henle’s fiber layer using spectral domain optical coherence tomography,” Invest. Ophthalmol. Visual Sci.52, 1486–1492 (2011).
[CrossRef]

Chen, M.

M. Chen, A. Carass, D. Reich, P. Calabresi, D. Pham, and J. Prince, “Voxel-wise displacement as independent features in classification of multiple sclerosis,” in “Proc. SPIE-MI 2013,” (Lake Buena Vista, FL, 2013).

M. Chen, A. Lang, E. Sotirchos, H. S. Ying, P. A. Calabresi, J. L. Prince, and A. Carass, “Deformable registration of macular oct using a-mode scan similarity,” in “Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on,” (IEEE, 2013), pp. 476–479.

Chiu, S. J.

A. N. Kuo, R. P. McNabb, S. J. Chiu, M. A. El-Dairi, S. Farsiu, C. A. Toth, and J. A. Izatt, “Correction of ocular shape in retinal optical coherence tomography and effect on current clinical measures,” Am. J. Ophthalmol.156, 304–311 (2013).
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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. Express18, 19413–19428 (2010).
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Christensen, G. E.

M. I. Miller, G. E. Christensen, Y. Amit, and U. Grenander, “Mathematical textbook of deformable neuroanatomies,” Proc. Natl. Acad. Sci.90, 11944–11948 (1993).
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Christensen, U.

T. M. Jørgensen, J. Thomadsen, U. Christensen, W. Soliman, and B. Sander, “Enhancing the signal-to-noise ratio in ophthalmic optical coherence tomography by image registration—method and clinical examples,” J. Biomed. Opt.12, 041208 (2007).
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Cigada, M.

A. Giani, M. Pellegrini, A. Invernizzi, M. Cigada, and G. Staurenghi, “Aligning scan locations from consecutive spectral-domain optical coherence tomography examinations: A comparison among different strategies,” Invest. Ophthalmol. Vis. Sci.53, 7637–7643 (2012).
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Conger, A.

J. N. Ratchford, S. Saidha, E. S. Sotirchos, J. A. Oh, M. A. Seigo, C. Eckstein, M. K. Durbin, J. D. Oakley, S. A. Meyer, A. Conger, T. C. Frohman, S. D. Newsome, L. J. Balcer, E. M. Frohman, and P. A. Calabresi, “Active MS is associated with accelerated retinal ganglion cell/inner plexiform layer thinning,” Neurology80, 47–54 (2013).
[CrossRef]

Cook, P. A.

B. B. Avants, N. J. Tustison, G. Song, P. A. Cook, A. Klein, and J. C. Gee, “A reproducible evaluation of ANTs similarity metric performance in brain image registration,” NeuroImage54, 2033–2044 (2011).
[CrossRef]

Correnti, A.

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,” Ophthalmology110, 177–189 (2003).
[CrossRef] [PubMed]

Crainiceanu, C. M.

S. Saidha, E. S. Sotirchos, J. Oh, S. B. Syc, M. A. Seigo, N. Shiee, C. Eckstein, M. K. Durbin, J. D. Oakley, S. A. Meyer, T. C. Frohman, S. Newsome, J. N. Ratchford, L. J. Balcer, D. L. Pham, C. M. Crainiceanu, E. M. Frohman, D. S. Reich, and P. A. Calabresi, “Relationships between retinal axonal and neuronal measures and global central nervous system pathology in Multiple Sclerosis,” JAMA Neurology70, 34–43 (2013).
[CrossRef] [PubMed]

S. Saidha, E. S. Sotirchos, M. A. Ibrahim, C. M. Crainiceanu, J. M. Gelfand, Y. J. Sepah, J. N. Ratchford, J. Oh, M. A. Seigo, S. D. Newsome, L. J. Balcer, E. M. Frohman, A. J. Green, Q. D. Nguyen, and P. A. Calabresi, “Microcystic macular oedema, thickness of the inner nuclear layer of the retina, and disease characteristics in multiple sclerosis: A retrospective study,” The Lancet Neurology11, 963–972 (2012).
[CrossRef]

Cutter, G.

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

Davatzikos, C.

A. Sotiras, C. Davatzikos, and N. Paragios, “Deformable medical image registration: A survey,” IEEE Trans. Med. Imag.32, 1153–1190 (2013).
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Y. Ou, A. Sotiras, N. Paragios, and C. Davatzikos, “Dramms: Deformable registration via attribute matching and mutual-saliency weighting,” Med. Image Anal.15, 622–639 (2011).
[CrossRef]

Y. Fan, S. M. Resnick, X. Wu, and C. Davatzikos, “Structural and functional biomarkers of prodromal Alzheimer’s disease: A high-dimensional pattern classification study,” NeuroImage41, 277–285 (2008).
[CrossRef] [PubMed]

S. M. Resnick, D. L. Pham, M. A. Kraut, A. Zonderman, and C. Davatzikos, “Longitudinal magnetic resonance imaging studies of older adults: A shrinking brain,” J. Neurosci.23, 3295–3301 (2003).
[PubMed]

C. Davatzikos, A. Genc, D. Xu, and S. M. Resnick, “Voxel-based morphometry using the RAVENS maps: Methods and validation using simulated longitudinal atrophy,” NeuroImage14, 1361–1369 (2001).
[CrossRef] [PubMed]

A. F. Goldszal, C. Davatzikos, D. Pham, M. X. H. Yan, R. N. Bryan, and S. M. Resnick, “An image-processing system for qualitative and quantitative volumetric analysis of brain images,” J. Computer Assisted Tomography22, 827–837 (1998).
[CrossRef]

Dawant, B. M.

G. K. Rohde, A. Aldroubi, and B. M. Dawant, “The adaptive bases algorithm for intensity based nonrigid image registration,” IEEE Trans. Med. Imag.22, 1470–1479 (2003).
[CrossRef]

Dawson, L. A.

K. K. Brock, M. B. Sharpe, L. A. Dawson, S. M. Kim, and D. A. Jaffray, “Accuracy of finite element model-based multi-organ deformable image registration,” Med. Phys.32, 1647–1659 (2005).
[CrossRef] [PubMed]

Del Turco, C.

G. Querques, R. Lattanzio, L. Querques, C. Del Turco, R. Forte, L. Pierro, E. H. Souied, and F. Bandello, “Enhanced depth imaging optical coherence tomography in Type 2 diabetes,” Invest. Ophthalmol. Vis. Sci.53, 6017–6024 (2012).
[CrossRef] [PubMed]

Dice, L. R.

L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology26, 297–302 (1945).
[CrossRef]

Drexler, W.

Durbin, M. K.

J. N. Ratchford, S. Saidha, E. S. Sotirchos, J. A. Oh, M. A. Seigo, C. Eckstein, M. K. Durbin, J. D. Oakley, S. A. Meyer, A. Conger, T. C. Frohman, S. D. Newsome, L. J. Balcer, E. M. Frohman, and P. A. Calabresi, “Active MS is associated with accelerated retinal ganglion cell/inner plexiform layer thinning,” Neurology80, 47–54 (2013).
[CrossRef]

S. Saidha, E. S. Sotirchos, J. Oh, S. B. Syc, M. A. Seigo, N. Shiee, C. Eckstein, M. K. Durbin, J. D. Oakley, S. A. Meyer, T. C. Frohman, S. Newsome, J. N. Ratchford, L. J. Balcer, D. L. Pham, C. M. Crainiceanu, E. M. Frohman, D. S. Reich, and P. A. Calabresi, “Relationships between retinal axonal and neuronal measures and global central nervous system pathology in Multiple Sclerosis,” JAMA Neurology70, 34–43 (2013).
[CrossRef] [PubMed]

Eckstein, C.

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J. Xu, H. Ishikawa, G. Wollstein, L. Kagemann, and J. S. Schuman, “Alignment of 3-d optical coherence tomography scans to correct eye movement using a particle filtering,” IEEE Trans. Med. Imag.31, 1337–1345 (2012).
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Biomed. Opt. Express (3)

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Figures (6)

Fig. 1
Fig. 1

The top row shows B-scans from two OCT images used as the subject and target. The second row shows example results when using the default settings for two generic deformable registration algorithms, SyN [19] and DRAMMS [39], to register the subject image to the target.

Fig. 2
Fig. 2

Shown are the outputs at each step of the registration algorithm, from the subject image �� at the top, to the results of the registration after (a) the rigid alignment to the fovea, (b) the affine alignment of the A-scans and (c) the deformable registration. The target image �� is shown at the bottom for reference. The bottom figures in (b) and (c) show checkerboard comparisons between each result and the target image.

Fig. 3
Fig. 3

B-scan (left) and en-face (right) views of an average atlas created from macular OCT images from 40 healthy control subjects. The green and red lines show the location of each view relative to the other. The vertical scale in the B-scan view is tripled to better show the details of the atlas.

Fig. 4
Fig. 4

Statistical atlases of the 8 retinal layers constructed from 40 healthy control subjects registered into a normalized space.

Fig. 5
Fig. 5

Shown are a subject image and an average atlas, their registrations to each other, and the subject’s RAVENS maps for each layer, overlaid on the average atlas. Each RAVENS map is shown in log scale to better illustrate where the tissue is expanding (blue) or compressing (red) relative to the average atlas. The vertical scale of the B-scan is doubled to better show the details of the maps.

Fig. 6
Fig. 6

SPM significance map (at an α level of 0.05) of RAVENS differences between controls and MS overlaid on the average atlas. Shown are a B-scan view (top) and three en-face views at different depths (bottom). The colored markers on the left side of the B-scan show the depth location of each en-face view. Colored lines indicate the boundaries between each layer. The vertical scale of the B-scan view is tripled to better show the significant areas respective to the average atlas.

Tables (2)

Tables Icon

Table 1 Dice overlap between segmentations transferred using a registration algorithm and the manual segmentation for eight retinal layers, averaged over 200 registrations (40 targets, 5 subjects). The deformable registrations were performed with SyN [19], A-OCT, and D-OCT. All eight layers and their mean were found to gained significant improvements (at an α level of 0.01) in Dice overlap when comparing SyN against either A-OCT or D-OCT. Asterisked (*) values on the D-OCT row indicate the layers that gained significant improvements in Dice when comparing A-OCT against D-OCT.

Tables Icon

Table 2 Average layer boundary surface errors (μm) between segmentations transferred using a registration algorithm and the manual segmentation for nine retinal layer boundaries, averaged over 200 registrations (40 targets, 5 subjects). The deformable registrations were performed with SyN [19], A-OCT, and D-OCT. All nine boundaries and their mean were found to have significantly less error (at an α level of 0.01) when comparing SyN against either A-OCT or D-OCT. Asterisked (*) values on the D-OCT rows indicate the boundaries that had significantly less error when comparing A-OCT against D-OCT.

Equations (12)

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𝒮 ˜ ( x ) = 𝒮 ( v ( x ) ) , x 𝔻 𝒯 ,
𝒮 ˜ ( x ) = 𝒮 ( r ( a m ( d m ( x ) ) ) ) , x 𝔻 𝒯 .
r ( x ) = x t ,
t = [ ( f 𝒮 ) x ( f 𝒯 ) x ( f 𝒮 ) y ( f 𝒯 ) y 0 ] .
a m ( x ) = F m x + t m ,
F m = [ 1 0 0 0 1 0 0 0 r m ] ,
t m = [ 0 0 i 𝒮 , m + b 𝒮 , m 2 r m ( i 𝒯 , m + b 𝒯 , m 2 ) ] .
r m = ( b 𝒮 , m i 𝒮 , m ) ( b 𝒯 , m i 𝒯 , m ) .
d m ( x ) = x + i c i Φ ( x x i ) ,
Φ ( x ) = [ 0 0 ϕ ( x s ) ] , where ϕ ( r ) = ( 1 r ) + 4 ( 3 r 3 + 12 r 2 + 16 r + 4 ) ,
E m SSD = { x | ( x , y ) = m } ( 𝒯 ( x ) 𝒮 ( A-OCT ) ( d m ( x ) ) ) 2 ,
E m Reg = r = R r 0 R { x | ( x , y ) = m + ( r , 0 ) } 1 | r | ( 𝒯 ( x ) 𝒮 ( A OCT ) ( d m ( x ) ) ) 2 ,

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