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Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 5, Iss. 7 — Jul. 1, 2014
  • pp: 2196–2214

Analysis of macular OCT images using deformable registration

Min Chen, Andrew Lang, Howard S. Ying, Peter A. Calabresi, Jerry L. Prince, and Aaron Carass  »View Author Affiliations


Biomedical Optics Express, Vol. 5, Issue 7, pp. 2196-2214 (2014)
http://dx.doi.org/10.1364/BOE.5.002196


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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

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

ToC Category:
Image Processing

History
Original Manuscript: April 21, 2014
Revised Manuscript: May 30, 2014
Manuscript Accepted: June 2, 2014
Published: June 11, 2014

Citation
Min Chen, Andrew Lang, Howard S. Ying, Peter A. Calabresi, Jerry L. Prince, and Aaron Carass, "Analysis of macular OCT images using deformable registration," Biomed. Opt. Express 5, 2196-2214 (2014)
http://www.opticsinfobase.org/boe/abstract.cfm?URI=boe-5-7-2196


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References

  1. 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]
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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]
  11. 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]
  12. 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]
  13. 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]
  14. 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).
  15. 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]
  16. A. Sotiras, C. Davatzikos, and N. Paragios, “Deformable medical image registration: A survey,” IEEE Trans. Med. Imag.32, 1153–1190 (2013). [CrossRef]
  17. M. I. Miller, G. E. Christensen, Y. Amit, and U. Grenander, “Mathematical textbook of deformable neuroanatomies,” Proc. Natl. Acad. Sci.90, 11944–11948 (1993). [CrossRef] [PubMed]
  18. J. Ashburner and K. J. Friston, “Voxel-based morphometry—the methods,” NeuroImage11, 805821 (2000). [CrossRef]
  19. 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]
  20. 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]
  21. 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]
  22. W. Bai and M. Brady, “Motion correction and attenuation correction for respiratory gated PET images,” IEEE Trans. Med. Imag.30, 351–365 (2011). [CrossRef]
  23. 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]
  24. 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]
  25. 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]
  26. 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]
  27. 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).
  28. 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).
  29. A. A. Goshtasby, 2-D and 3-D Image Registration: For Medical, Remote Sensing, and Industrial Applications (Wiley, 2005).
  30. 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). [CrossRef] [PubMed]
  31. 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]
  32. 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]
  33. 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]
  34. 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]
  35. 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.
  36. 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]
  37. 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]
  38. Y. Zheng, R. Xiao, Y. Wang, and J. C. Gee, “A generative model for oct retinal layer segmentation by integrating graph-based multi-surface searching and image registration,” in “16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2013),” (Springer, 2013), pp. 428–435.
  39. 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]
  40. 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]
  41. 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]
  42. A. Guimond, J. Meunier, and J.-P. Thirion, “Average brain models: A convergence study,” Computer vision and image understanding77, 192–210 (2000). [CrossRef]
  43. B. Avants and J. C. Gee, “Geodesic estimation for large deformation anatomical shape averaging and interpolation,” NeuroImage23, S139–S150 (2004). [CrossRef] [PubMed]
  44. 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]
  45. 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]
  46. 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).
  47. 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]
  48. 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]
  49. 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]
  50. 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]
  51. 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]
  52. L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology26, 297–302 (1945). [CrossRef]
  53. K. J. Friston, J. Ashburner, S. Kiebel, T. Nichols, and W. Penny, eds., Statistical Parametric Mapping: The Analysis of Functional Brain Images (Academic Press, 2007).
  54. J. B. Kerrison, T. Flynn, and W. R. Green, “Retinal pathologic changes in multiple sclerosis,” Retina14, 445–451 (1994). [CrossRef] [PubMed]
  55. 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]
  56. 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]
  57. 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]

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