Abstract

Adaptive optics (AO) scanning laser ophthalmoscopy offers a non-invasive approach for observing the retina at a cellular level. Its high resolution capabilities have direct application for monitoring and treating retinal diseases by providing quantitative assessment of cone health and density across time. However, accurate longitudinal analysis of AO images requires that AO images from different sessions be aligned, such that cell-to-cell correspondences can be established between timepoints. Such alignment is currently done manually, a time intensive task that is restrictive for large longitudinal AO studies. Automated longitudinal montaging for AO images remains a challenge because the intensity pattern of imaged cone mosaics can vary significantly, even across short timespans. This limitation prevents existing intensity-based montaging approaches from being accurately applied to longitudinal AO images. In the present work, we address this problem by presenting a constellation-based method for performing longitudinal alignment of AO images. Rather than matching intensity similarities between images, our approach finds structural patterns in the cone mosaics and leverages these to calculate the correct alignment. These structural patterns are robust to intensity variations, allowing us to make accurate longitudinal alignments. We validate our algorithm using 8 longitudinal AO datasets, each with two timepoints separated 6–12 months apart. Our results show that the proposed method can produce longitudinal AO montages with cell-to-cell correspondences across the full extent of the montage. Quantitative assessment of the alignment accuracy shows that the algorithm is able to find longitudinal alignments whose accuracy is on par with manual alignments performed by a trained rater.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Full Article  |  PDF Article
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References

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K. Jackson, G. K. Vergilio, R. F. Cooper, G.-S. Ying, and J. I. Morgan, “A 2-year longitudinal study of normal cone photoreceptor density,” Invest. Ophthalmol. Vis. Sci. 60(5), 1420–1430 (2019).
[Crossref]

2018 (3)

K. G. Foote, P. Loumou, S. Griffin, J. Qin, K. Ratnam, T. C. Porco, A. Roorda, and J. L. Duncan, “Relationship between foveal cone structure and visual acuity measured with adaptive optics scanning laser ophthalmoscopy in retinal degeneration,” Invest. Ophthalmol. Vis. Sci. 59(8), 3385–3393 (2018).
[Crossref]

J. I. Morgan, G. K. Vergilio, J. Hsu, A. Dubra, and R. F. Cooper, “The reliability of cone density measurements in the presence of rods,” Trans. Vis. Sci. Tech. 7(3), 21 (2018).
[Crossref]

B. Davidson, A. Kalitzeos, J. Carroll, A. Dubra, S. Ourselin, M. Michaelides, and C. Bergeles, “Fast adaptive optics scanning light ophthalmoscope retinal montaging,” Biomed. Opt. Express 9(9), 4317–4328 (2018).
[Crossref]

2017 (5)

A. E. Salmon, R. F. Cooper, C. S. Langlo, A. Baghaie, A. Dubra, and J. Carroll, “An automated reference frame selection (arfs) algorithm for cone imaging with adaptive optics scanning light ophthalmoscopy,” Trans. Vis. Sci. Tech. 6(2), 9 (2017).
[Crossref]

P. Bedggood and A. Metha, “De-warping of images and improved eye tracking for the scanning laser ophthalmoscope,” PLoS One 12(4), e0174617 (2017).
[Crossref]

E. A. Hernández, M. A. Alonso, E. Chávez, D. H. Covarrubias, and R. Conte, “Robust polygon recognition method with similarity invariants applied to star identification,” Adv. Space Res. 59(4), 1095–1111 (2017).
[Crossref]

D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (2017).
[Crossref]

C. S. Langlo, L. R. Erker, M. Parker, E. J. Patterson, B. P. Higgins, P. Summerfelt, M. M. Razeen, F. T. Collison, G. A. Fishman, C. N. Kay, J. Zhang, R. G. Weleber, P. Yang, M. E. Pennesi, B. L. Lam, J. D. Chulay, A. Dubra, W. W. Hauswirth, D. J. Wilson, and J. Carroll, “Repeatability and longitudinal assessment of foveal cone structure in cngb3-associated achromatopsia,” Retina 37(10), 1956–1966 (2017).
[Crossref]

2016 (3)

T. Y. P. Chui, A. Pinhas, A. Gan, M. Razeen, N. Shah, E. Cheang, C. L. Liu, A. Dubra, and R. B. Rosen, “Longitudinal imaging of microvascular remodelling in proliferative diabetic retinopathy using adaptive optics scanning light ophthalmoscopy,” Ophthal Physl. Opt. 36(3), 290–302 (2016).
[Crossref]

L. Mariotti, N. Devaney, G. Lombardo, and M. Lombardo, “Understanding the changes of cone reflectance in adaptive optics flood illumination retinal images over three years,” Biomed. Opt. Express 7(7), 2807–2822 (2016).
[Crossref]

M. Chen, R. F. Cooper, G. K. Han, J. Gee, D. H. Brainard, and J. I. Morgan, “Multi-modal automatic montaging of adaptive optics retinal images,” Biomed. Opt. Express 7(12), 4899 (2016).
[Crossref]

2015 (1)

S. Hansen, S. Batson, K. M. Weinlander, R. F. Cooper, D. H. Scoles, P. A. Karth, D. V. Weinberg, A. Dubra, J. E. Kim, J. Carroll, and W. J. Wirostko, “Assessing photoreceptor structure following macular hole closure,” Retin. Cases Brief Rep. 9(1), 15–20 (2015).
[Crossref]

2014 (1)

D. Scoles, Y. N. Sulai, C. S. Langlo, G. A. Fishman, C. A. Curcio, J. Carroll, and A. Dubra, “In vivo imaging of human cone photoreceptor inner segments,” Invest. Ophthalmol. Vis. Sci. 55(7), 4244–4251 (2014).
[Crossref]

2013 (1)

2012 (2)

H. Li, J. Lu, G. Shi, and Y. Zhang, “Automatic montage of retinal images in adaptive optics confocal scanning laser ophthalmoscope,” Opt. Eng. 51(5), 057008 (2012).
[Crossref]

R. Garrioch, C. Langlo, A. M. Dubis, R. F. Cooper, A. Dubra, and J. Carroll, “The repeatability of in vivo parafoveal cone density and spacing measurements,” Optom. Vis. Sci. 89(5), 632–643 (2012).
[Crossref]

2011 (2)

K. E. Talcott, K. Ratnam, S. M. Sundquist, A. S. Lucero, B. J. Lujan, W. Tao, T. C. Porco, A. Roorda, and J. L. Duncan, “Longitudinal study of cone photoreceptors during retinal degeneration and in response to ciliary neurotrophic factor treatment,” Invest. Ophthalmol. Vis. Sci. 52(5), 2219–2226 (2011).
[Crossref]

A. Dubra and Y. Sulai, “Reflective afocal broadband adaptive optics scanning ophthalmoscope,” Biomed. Opt. Express 2(6), 1757–1768 (2011).
[Crossref]

2003 (2)

J. P. Pluim, J. A. Maintz, and M. A. Viergever, “Mutual-information-based registration of medical images: a survey,” IEEE Trans. Med. Imag. 22(8), 986–1004 (2003).
[Crossref]

A. Pallikaris, D. R. Williams, and H. Hofer, “The reflectance of single cones in the living human eye,” Invest. Ophthalmol. Vis. Sci. 44(10), 4580–4592 (2003).
[Crossref]

1999 (1)

C. Studholme, D. L. Hill, and D. J. Hawkes, “An overlap invariant entropy measure of 3D medical image alignment,” Pattern Recogn. 32(1), 71–86 (1999).
[Crossref]

1997 (1)

C. Padgett and K. Kreutz-Delgado, “A grid algorithm for autonomous star identification,” IEEE Trans. Aerosp. Electron. Syst. 33(1), 202–213 (1997).
[Crossref]

1981 (1)

M. A. Fischler and R. C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM 24(6), 381–395 (1981).
[Crossref]

Alonso, M. A.

E. A. Hernández, M. A. Alonso, E. Chávez, D. H. Covarrubias, and R. Conte, “Robust polygon recognition method with similarity invariants applied to star identification,” Adv. Space Res. 59(4), 1095–1111 (2017).
[Crossref]

Baghaie, A.

A. E. Salmon, R. F. Cooper, C. S. Langlo, A. Baghaie, A. Dubra, and J. Carroll, “An automated reference frame selection (arfs) algorithm for cone imaging with adaptive optics scanning light ophthalmoscopy,” Trans. Vis. Sci. Tech. 6(2), 9 (2017).
[Crossref]

Batson, S.

S. Hansen, S. Batson, K. M. Weinlander, R. F. Cooper, D. H. Scoles, P. A. Karth, D. V. Weinberg, A. Dubra, J. E. Kim, J. Carroll, and W. J. Wirostko, “Assessing photoreceptor structure following macular hole closure,” Retin. Cases Brief Rep. 9(1), 15–20 (2015).
[Crossref]

Bedggood, P.

P. Bedggood and A. Metha, “De-warping of images and improved eye tracking for the scanning laser ophthalmoscope,” PLoS One 12(4), e0174617 (2017).
[Crossref]

Bergeles, C.

Bolles, R. C.

M. A. Fischler and R. C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Commun. ACM 24(6), 381–395 (1981).
[Crossref]

Brainard, D. H.

Carroll, J.

B. Davidson, A. Kalitzeos, J. Carroll, A. Dubra, S. Ourselin, M. Michaelides, and C. Bergeles, “Fast adaptive optics scanning light ophthalmoscope retinal montaging,” Biomed. Opt. Express 9(9), 4317–4328 (2018).
[Crossref]

C. S. Langlo, L. R. Erker, M. Parker, E. J. Patterson, B. P. Higgins, P. Summerfelt, M. M. Razeen, F. T. Collison, G. A. Fishman, C. N. Kay, J. Zhang, R. G. Weleber, P. Yang, M. E. Pennesi, B. L. Lam, J. D. Chulay, A. Dubra, W. W. Hauswirth, D. J. Wilson, and J. Carroll, “Repeatability and longitudinal assessment of foveal cone structure in cngb3-associated achromatopsia,” Retina 37(10), 1956–1966 (2017).
[Crossref]

A. E. Salmon, R. F. Cooper, C. S. Langlo, A. Baghaie, A. Dubra, and J. Carroll, “An automated reference frame selection (arfs) algorithm for cone imaging with adaptive optics scanning light ophthalmoscopy,” Trans. Vis. Sci. Tech. 6(2), 9 (2017).
[Crossref]

D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (2017).
[Crossref]

S. Hansen, S. Batson, K. M. Weinlander, R. F. Cooper, D. H. Scoles, P. A. Karth, D. V. Weinberg, A. Dubra, J. E. Kim, J. Carroll, and W. J. Wirostko, “Assessing photoreceptor structure following macular hole closure,” Retin. Cases Brief Rep. 9(1), 15–20 (2015).
[Crossref]

D. Scoles, Y. N. Sulai, C. S. Langlo, G. A. Fishman, C. A. Curcio, J. Carroll, and A. Dubra, “In vivo imaging of human cone photoreceptor inner segments,” Invest. Ophthalmol. Vis. Sci. 55(7), 4244–4251 (2014).
[Crossref]

R. Garrioch, C. Langlo, A. M. Dubis, R. F. Cooper, A. Dubra, and J. Carroll, “The repeatability of in vivo parafoveal cone density and spacing measurements,” Optom. Vis. Sci. 89(5), 632–643 (2012).
[Crossref]

Chávez, E.

E. A. Hernández, M. A. Alonso, E. Chávez, D. H. Covarrubias, and R. Conte, “Robust polygon recognition method with similarity invariants applied to star identification,” Adv. Space Res. 59(4), 1095–1111 (2017).
[Crossref]

Cheang, E.

T. Y. P. Chui, A. Pinhas, A. Gan, M. Razeen, N. Shah, E. Cheang, C. L. Liu, A. Dubra, and R. B. Rosen, “Longitudinal imaging of microvascular remodelling in proliferative diabetic retinopathy using adaptive optics scanning light ophthalmoscopy,” Ophthal Physl. Opt. 36(3), 290–302 (2016).
[Crossref]

Chen, M.

Chen, Z.

Y. Duan, Z. Niu, and Z. Chen, “A star identification algorithm for large fov observations,” in “Image and Signal Processing for Remote Sensing XXII,” vol. 10004 (International Society for Optics and Photonics, 2016), p. 100041G.

Chui, T. Y. P.

T. Y. P. Chui, A. Pinhas, A. Gan, M. Razeen, N. Shah, E. Cheang, C. L. Liu, A. Dubra, and R. B. Rosen, “Longitudinal imaging of microvascular remodelling in proliferative diabetic retinopathy using adaptive optics scanning light ophthalmoscopy,” Ophthal Physl. Opt. 36(3), 290–302 (2016).
[Crossref]

Chulay, J. D.

C. S. Langlo, L. R. Erker, M. Parker, E. J. Patterson, B. P. Higgins, P. Summerfelt, M. M. Razeen, F. T. Collison, G. A. Fishman, C. N. Kay, J. Zhang, R. G. Weleber, P. Yang, M. E. Pennesi, B. L. Lam, J. D. Chulay, A. Dubra, W. W. Hauswirth, D. J. Wilson, and J. Carroll, “Repeatability and longitudinal assessment of foveal cone structure in cngb3-associated achromatopsia,” Retina 37(10), 1956–1966 (2017).
[Crossref]

Collison, F. T.

C. S. Langlo, L. R. Erker, M. Parker, E. J. Patterson, B. P. Higgins, P. Summerfelt, M. M. Razeen, F. T. Collison, G. A. Fishman, C. N. Kay, J. Zhang, R. G. Weleber, P. Yang, M. E. Pennesi, B. L. Lam, J. D. Chulay, A. Dubra, W. W. Hauswirth, D. J. Wilson, and J. Carroll, “Repeatability and longitudinal assessment of foveal cone structure in cngb3-associated achromatopsia,” Retina 37(10), 1956–1966 (2017).
[Crossref]

Conte, R.

E. A. Hernández, M. A. Alonso, E. Chávez, D. H. Covarrubias, and R. Conte, “Robust polygon recognition method with similarity invariants applied to star identification,” Adv. Space Res. 59(4), 1095–1111 (2017).
[Crossref]

Cooper, R. F.

K. Jackson, G. K. Vergilio, R. F. Cooper, G.-S. Ying, and J. I. Morgan, “A 2-year longitudinal study of normal cone photoreceptor density,” Invest. Ophthalmol. Vis. Sci. 60(5), 1420–1430 (2019).
[Crossref]

J. I. Morgan, G. K. Vergilio, J. Hsu, A. Dubra, and R. F. Cooper, “The reliability of cone density measurements in the presence of rods,” Trans. Vis. Sci. Tech. 7(3), 21 (2018).
[Crossref]

A. E. Salmon, R. F. Cooper, C. S. Langlo, A. Baghaie, A. Dubra, and J. Carroll, “An automated reference frame selection (arfs) algorithm for cone imaging with adaptive optics scanning light ophthalmoscopy,” Trans. Vis. Sci. Tech. 6(2), 9 (2017).
[Crossref]

D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (2017).
[Crossref]

M. Chen, R. F. Cooper, G. K. Han, J. Gee, D. H. Brainard, and J. I. Morgan, “Multi-modal automatic montaging of adaptive optics retinal images,” Biomed. Opt. Express 7(12), 4899 (2016).
[Crossref]

S. Hansen, S. Batson, K. M. Weinlander, R. F. Cooper, D. H. Scoles, P. A. Karth, D. V. Weinberg, A. Dubra, J. E. Kim, J. Carroll, and W. J. Wirostko, “Assessing photoreceptor structure following macular hole closure,” Retin. Cases Brief Rep. 9(1), 15–20 (2015).
[Crossref]

R. Garrioch, C. Langlo, A. M. Dubis, R. F. Cooper, A. Dubra, and J. Carroll, “The repeatability of in vivo parafoveal cone density and spacing measurements,” Optom. Vis. Sci. 89(5), 632–643 (2012).
[Crossref]

Covarrubias, D. H.

E. A. Hernández, M. A. Alonso, E. Chávez, D. H. Covarrubias, and R. Conte, “Robust polygon recognition method with similarity invariants applied to star identification,” Adv. Space Res. 59(4), 1095–1111 (2017).
[Crossref]

Cunefare, D.

D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (2017).
[Crossref]

Curcio, C. A.

D. Scoles, Y. N. Sulai, C. S. Langlo, G. A. Fishman, C. A. Curcio, J. Carroll, and A. Dubra, “In vivo imaging of human cone photoreceptor inner segments,” Invest. Ophthalmol. Vis. Sci. 55(7), 4244–4251 (2014).
[Crossref]

Davidson, B.

Devaney, N.

Duan, Y.

Y. Duan, Z. Niu, and Z. Chen, “A star identification algorithm for large fov observations,” in “Image and Signal Processing for Remote Sensing XXII,” vol. 10004 (International Society for Optics and Photonics, 2016), p. 100041G.

Dubis, A. M.

R. Garrioch, C. Langlo, A. M. Dubis, R. F. Cooper, A. Dubra, and J. Carroll, “The repeatability of in vivo parafoveal cone density and spacing measurements,” Optom. Vis. Sci. 89(5), 632–643 (2012).
[Crossref]

Dubra, A.

J. I. Morgan, G. K. Vergilio, J. Hsu, A. Dubra, and R. F. Cooper, “The reliability of cone density measurements in the presence of rods,” Trans. Vis. Sci. Tech. 7(3), 21 (2018).
[Crossref]

B. Davidson, A. Kalitzeos, J. Carroll, A. Dubra, S. Ourselin, M. Michaelides, and C. Bergeles, “Fast adaptive optics scanning light ophthalmoscope retinal montaging,” Biomed. Opt. Express 9(9), 4317–4328 (2018).
[Crossref]

C. S. Langlo, L. R. Erker, M. Parker, E. J. Patterson, B. P. Higgins, P. Summerfelt, M. M. Razeen, F. T. Collison, G. A. Fishman, C. N. Kay, J. Zhang, R. G. Weleber, P. Yang, M. E. Pennesi, B. L. Lam, J. D. Chulay, A. Dubra, W. W. Hauswirth, D. J. Wilson, and J. Carroll, “Repeatability and longitudinal assessment of foveal cone structure in cngb3-associated achromatopsia,” Retina 37(10), 1956–1966 (2017).
[Crossref]

A. E. Salmon, R. F. Cooper, C. S. Langlo, A. Baghaie, A. Dubra, and J. Carroll, “An automated reference frame selection (arfs) algorithm for cone imaging with adaptive optics scanning light ophthalmoscopy,” Trans. Vis. Sci. Tech. 6(2), 9 (2017).
[Crossref]

D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (2017).
[Crossref]

T. Y. P. Chui, A. Pinhas, A. Gan, M. Razeen, N. Shah, E. Cheang, C. L. Liu, A. Dubra, and R. B. Rosen, “Longitudinal imaging of microvascular remodelling in proliferative diabetic retinopathy using adaptive optics scanning light ophthalmoscopy,” Ophthal Physl. Opt. 36(3), 290–302 (2016).
[Crossref]

S. Hansen, S. Batson, K. M. Weinlander, R. F. Cooper, D. H. Scoles, P. A. Karth, D. V. Weinberg, A. Dubra, J. E. Kim, J. Carroll, and W. J. Wirostko, “Assessing photoreceptor structure following macular hole closure,” Retin. Cases Brief Rep. 9(1), 15–20 (2015).
[Crossref]

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K. E. Talcott, K. Ratnam, S. M. Sundquist, A. S. Lucero, B. J. Lujan, W. Tao, T. C. Porco, A. Roorda, and J. L. Duncan, “Longitudinal study of cone photoreceptors during retinal degeneration and in response to ciliary neurotrophic factor treatment,” Invest. Ophthalmol. Vis. Sci. 52(5), 2219–2226 (2011).
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K. E. Talcott, K. Ratnam, S. M. Sundquist, A. S. Lucero, B. J. Lujan, W. Tao, T. C. Porco, A. Roorda, and J. L. Duncan, “Longitudinal study of cone photoreceptors during retinal degeneration and in response to ciliary neurotrophic factor treatment,” Invest. Ophthalmol. Vis. Sci. 52(5), 2219–2226 (2011).
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K. E. Talcott, K. Ratnam, S. M. Sundquist, A. S. Lucero, B. J. Lujan, W. Tao, T. C. Porco, A. Roorda, and J. L. Duncan, “Longitudinal study of cone photoreceptors during retinal degeneration and in response to ciliary neurotrophic factor treatment,” Invest. Ophthalmol. Vis. Sci. 52(5), 2219–2226 (2011).
[Crossref]

Vergilio, G. K.

K. Jackson, G. K. Vergilio, R. F. Cooper, G.-S. Ying, and J. I. Morgan, “A 2-year longitudinal study of normal cone photoreceptor density,” Invest. Ophthalmol. Vis. Sci. 60(5), 1420–1430 (2019).
[Crossref]

J. I. Morgan, G. K. Vergilio, J. Hsu, A. Dubra, and R. F. Cooper, “The reliability of cone density measurements in the presence of rods,” Trans. Vis. Sci. Tech. 7(3), 21 (2018).
[Crossref]

Viergever, M. A.

J. P. Pluim, J. A. Maintz, and M. A. Viergever, “Mutual-information-based registration of medical images: a survey,” IEEE Trans. Med. Imag. 22(8), 986–1004 (2003).
[Crossref]

Weinberg, D. V.

S. Hansen, S. Batson, K. M. Weinlander, R. F. Cooper, D. H. Scoles, P. A. Karth, D. V. Weinberg, A. Dubra, J. E. Kim, J. Carroll, and W. J. Wirostko, “Assessing photoreceptor structure following macular hole closure,” Retin. Cases Brief Rep. 9(1), 15–20 (2015).
[Crossref]

Weinlander, K. M.

S. Hansen, S. Batson, K. M. Weinlander, R. F. Cooper, D. H. Scoles, P. A. Karth, D. V. Weinberg, A. Dubra, J. E. Kim, J. Carroll, and W. J. Wirostko, “Assessing photoreceptor structure following macular hole closure,” Retin. Cases Brief Rep. 9(1), 15–20 (2015).
[Crossref]

Weleber, R. G.

C. S. Langlo, L. R. Erker, M. Parker, E. J. Patterson, B. P. Higgins, P. Summerfelt, M. M. Razeen, F. T. Collison, G. A. Fishman, C. N. Kay, J. Zhang, R. G. Weleber, P. Yang, M. E. Pennesi, B. L. Lam, J. D. Chulay, A. Dubra, W. W. Hauswirth, D. J. Wilson, and J. Carroll, “Repeatability and longitudinal assessment of foveal cone structure in cngb3-associated achromatopsia,” Retina 37(10), 1956–1966 (2017).
[Crossref]

Williams, D. R.

A. Pallikaris, D. R. Williams, and H. Hofer, “The reflectance of single cones in the living human eye,” Invest. Ophthalmol. Vis. Sci. 44(10), 4580–4592 (2003).
[Crossref]

Wilson, D. J.

C. S. Langlo, L. R. Erker, M. Parker, E. J. Patterson, B. P. Higgins, P. Summerfelt, M. M. Razeen, F. T. Collison, G. A. Fishman, C. N. Kay, J. Zhang, R. G. Weleber, P. Yang, M. E. Pennesi, B. L. Lam, J. D. Chulay, A. Dubra, W. W. Hauswirth, D. J. Wilson, and J. Carroll, “Repeatability and longitudinal assessment of foveal cone structure in cngb3-associated achromatopsia,” Retina 37(10), 1956–1966 (2017).
[Crossref]

Wirostko, W. J.

S. Hansen, S. Batson, K. M. Weinlander, R. F. Cooper, D. H. Scoles, P. A. Karth, D. V. Weinberg, A. Dubra, J. E. Kim, J. Carroll, and W. J. Wirostko, “Assessing photoreceptor structure following macular hole closure,” Retin. Cases Brief Rep. 9(1), 15–20 (2015).
[Crossref]

Yang, P.

C. S. Langlo, L. R. Erker, M. Parker, E. J. Patterson, B. P. Higgins, P. Summerfelt, M. M. Razeen, F. T. Collison, G. A. Fishman, C. N. Kay, J. Zhang, R. G. Weleber, P. Yang, M. E. Pennesi, B. L. Lam, J. D. Chulay, A. Dubra, W. W. Hauswirth, D. J. Wilson, and J. Carroll, “Repeatability and longitudinal assessment of foveal cone structure in cngb3-associated achromatopsia,” Retina 37(10), 1956–1966 (2017).
[Crossref]

Ying, G.-S.

K. Jackson, G. K. Vergilio, R. F. Cooper, G.-S. Ying, and J. I. Morgan, “A 2-year longitudinal study of normal cone photoreceptor density,” Invest. Ophthalmol. Vis. Sci. 60(5), 1420–1430 (2019).
[Crossref]

Zhang, J.

C. S. Langlo, L. R. Erker, M. Parker, E. J. Patterson, B. P. Higgins, P. Summerfelt, M. M. Razeen, F. T. Collison, G. A. Fishman, C. N. Kay, J. Zhang, R. G. Weleber, P. Yang, M. E. Pennesi, B. L. Lam, J. D. Chulay, A. Dubra, W. W. Hauswirth, D. J. Wilson, and J. Carroll, “Repeatability and longitudinal assessment of foveal cone structure in cngb3-associated achromatopsia,” Retina 37(10), 1956–1966 (2017).
[Crossref]

Zhang, Y.

H. Li, J. Lu, G. Shi, and Y. Zhang, “Automatic montage of retinal images in adaptive optics confocal scanning laser ophthalmoscope,” Opt. Eng. 51(5), 057008 (2012).
[Crossref]

Adv. Space Res. (1)

E. A. Hernández, M. A. Alonso, E. Chávez, D. H. Covarrubias, and R. Conte, “Robust polygon recognition method with similarity invariants applied to star identification,” Adv. Space Res. 59(4), 1095–1111 (2017).
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IEEE Trans. Med. Imag. (1)

J. P. Pluim, J. A. Maintz, and M. A. Viergever, “Mutual-information-based registration of medical images: a survey,” IEEE Trans. Med. Imag. 22(8), 986–1004 (2003).
[Crossref]

Invest. Ophthalmol. Vis. Sci. (5)

D. Scoles, Y. N. Sulai, C. S. Langlo, G. A. Fishman, C. A. Curcio, J. Carroll, and A. Dubra, “In vivo imaging of human cone photoreceptor inner segments,” Invest. Ophthalmol. Vis. Sci. 55(7), 4244–4251 (2014).
[Crossref]

K. G. Foote, P. Loumou, S. Griffin, J. Qin, K. Ratnam, T. C. Porco, A. Roorda, and J. L. Duncan, “Relationship between foveal cone structure and visual acuity measured with adaptive optics scanning laser ophthalmoscopy in retinal degeneration,” Invest. Ophthalmol. Vis. Sci. 59(8), 3385–3393 (2018).
[Crossref]

A. Pallikaris, D. R. Williams, and H. Hofer, “The reflectance of single cones in the living human eye,” Invest. Ophthalmol. Vis. Sci. 44(10), 4580–4592 (2003).
[Crossref]

K. Jackson, G. K. Vergilio, R. F. Cooper, G.-S. Ying, and J. I. Morgan, “A 2-year longitudinal study of normal cone photoreceptor density,” Invest. Ophthalmol. Vis. Sci. 60(5), 1420–1430 (2019).
[Crossref]

K. E. Talcott, K. Ratnam, S. M. Sundquist, A. S. Lucero, B. J. Lujan, W. Tao, T. C. Porco, A. Roorda, and J. L. Duncan, “Longitudinal study of cone photoreceptors during retinal degeneration and in response to ciliary neurotrophic factor treatment,” Invest. Ophthalmol. Vis. Sci. 52(5), 2219–2226 (2011).
[Crossref]

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T. Y. P. Chui, A. Pinhas, A. Gan, M. Razeen, N. Shah, E. Cheang, C. L. Liu, A. Dubra, and R. B. Rosen, “Longitudinal imaging of microvascular remodelling in proliferative diabetic retinopathy using adaptive optics scanning light ophthalmoscopy,” Ophthal Physl. Opt. 36(3), 290–302 (2016).
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Opt. Eng. (1)

H. Li, J. Lu, G. Shi, and Y. Zhang, “Automatic montage of retinal images in adaptive optics confocal scanning laser ophthalmoscope,” Opt. Eng. 51(5), 057008 (2012).
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R. Garrioch, C. Langlo, A. M. Dubis, R. F. Cooper, A. Dubra, and J. Carroll, “The repeatability of in vivo parafoveal cone density and spacing measurements,” Optom. Vis. Sci. 89(5), 632–643 (2012).
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Retin. Cases Brief Rep. (1)

S. Hansen, S. Batson, K. M. Weinlander, R. F. Cooper, D. H. Scoles, P. A. Karth, D. V. Weinberg, A. Dubra, J. E. Kim, J. Carroll, and W. J. Wirostko, “Assessing photoreceptor structure following macular hole closure,” Retin. Cases Brief Rep. 9(1), 15–20 (2015).
[Crossref]

Retina (1)

C. S. Langlo, L. R. Erker, M. Parker, E. J. Patterson, B. P. Higgins, P. Summerfelt, M. M. Razeen, F. T. Collison, G. A. Fishman, C. N. Kay, J. Zhang, R. G. Weleber, P. Yang, M. E. Pennesi, B. L. Lam, J. D. Chulay, A. Dubra, W. W. Hauswirth, D. J. Wilson, and J. Carroll, “Repeatability and longitudinal assessment of foveal cone structure in cngb3-associated achromatopsia,” Retina 37(10), 1956–1966 (2017).
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Sci. Rep. (1)

D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (2017).
[Crossref]

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J. I. Morgan, G. K. Vergilio, J. Hsu, A. Dubra, and R. F. Cooper, “The reliability of cone density measurements in the presence of rods,” Trans. Vis. Sci. Tech. 7(3), 21 (2018).
[Crossref]

A. E. Salmon, R. F. Cooper, C. S. Langlo, A. Baghaie, A. Dubra, and J. Carroll, “An automated reference frame selection (arfs) algorithm for cone imaging with adaptive optics scanning light ophthalmoscopy,” Trans. Vis. Sci. Tech. 6(2), 9 (2017).
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A. Dubra and Z. Harvey, “Registration of 2D images from fast scanning ophthalmic instruments,” in “Proc. of 2010 International Workshop on Biomedical Image Registration,” (Springer, 2010), pp. 60–71

A. Standard, “American national standard for the safe use of lasers. American National Standards Institute, Inc.,” New York (1993).

Y. Duan, Z. Niu, and Z. Chen, “A star identification algorithm for large fov observations,” in “Image and Signal Processing for Remote Sensing XXII,” vol. 10004 (International Society for Optics and Photonics, 2016), p. 100041G.

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Figures (9)

Fig. 1.
Fig. 1. Examples of image differences between AOSLO images of the same subject and retinal location between two timepoints. (a) shows individual cone intensity changes (circled in yellow) across two timepoints. (b) shows the shift of vessel shadows (outlined in red and green) relative to the corresponding cone mosaics shown in (a). The vessel outlines were found semi-automatically by Gaussian smoothing each image and then adjusting a threshold to segment the vessel regions.
Fig. 2.
Fig. 2. Visual example of how a constellation feature is constructed and compared between two images: (a) cone centers detected for the pair of longitudinal images shown in Fig. 1; (b) zoomed in regions of theboxes shown in each image in (a); (c) grid representation of the constellation feature for the center cone of each region shown in (b); (d) difference in the grid patterns for the orange region from Timepoint 1 and each region from Timepoint 2 (Green-T1 only, Magenta-T2 only, White-Both); (e) the original image intensities within each region; (f) difference in intensities within the orange region for T1 and each region for T2 (Green-T1 higher intensity, Magenta-T2 higher intensity, Gray-Similar intensities in both image). The region indicated by orange at T2 has the highest match score. Inspection of (e) and (f) indicates that it is a good match.
Fig. 3.
Fig. 3. Example of an alignment between two longitudinal images using the constellation feature. (a) shows the total matches found. (b) shows the inlier matches found after RANSAC. (c) shows the location of features found in each image, with those in red showing the features in the two images corresponding to the inlier matches. (d) shows the average of the two images after alignment.
Fig. 4.
Fig. 4. Example of a longitudinal montage between two AO datasets collected a year apart: (a) montages of the confocal images from each timepoint; (b) zoomed in regions showing the cell-to-cell correspondence after alignment. The first and third rows show the confocal modality of the montage, and the second and fourth rows show the split detection modality.
Fig. 5.
Fig. 5. Maps of the NMI in the overlapping regions of the second timepoint montage after longitudinal alignment to the first timepoint using (a) our proposed constellation method and (b) a SIFT-based approach. Missing areas in the map in (b) indicate areas where the images could not be aligned due to not finding enough inlier matches to meet the alignment criterion. (c) and (d) show the average confocal intensity of the overlapping images located at the white boxes in (a) and (b), respectively.
Fig. 6.
Fig. 6. Matrices showing (a) the mean image similarity (NMI) after the alignment when using different constellation sizes (x-axis) and grid size (y-axis) for aligning the 20 development image pairs. (b) shows the mean image similarity when the image pairs are binned according to the eccentricity (in degrees) at which they were acquired.
Fig. 7.
Fig. 7. Comparison of the NMI image similarity (across 20 image pairs) after longitudinal alignment by SIFT [7] and the proposed constellation algorithm when using different automated cone detection inputs (Garrioch et al. [15] or Cunefare et al. [16]), and different AO imaging modalities as inputs (confocal only, split detection only, or both). The box plot show the median, 25th and 75th percentiles, and non-outlier range (whiskers) of the data. Blue circles show the outlier points, and the red diamond show the mean of the data.
Fig. 8.
Fig. 8. Analysis of the robustness of the constellation features. Each plot shows the median (across the 20 test pairs) of the relationship between the percent cone loss in the input image and the total number of (a) inlier (larger is better) and (b) total (larger is better) matches found, (c) percent of total matches that are inlier (larger is better), (d) NMI between the images after alignment (larger is better), and the (e) rotational and (f) translational errors (smaller is better) relative to when there are no simulated cone loss.
Fig. 9.
Fig. 9. Comparison between the manual and automated alignment for the 20 withheld test dataset image pairs. Shown are the plots for the rotation, scale and translation values for the alignment transformation, and the intensity similarity (using NMI) of the overlapping regions after each alignment.

Equations (23)

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

x a = T b a ( x b )   ,
I a b ( x b ) = I a ( T b a ( x b ) )
O n = { c x : | | c n c x | | < Q , x { 1 , 2 , N } }   .
d = { O n : n { 1 , 2 , N } }   .
b n ( l , k ) = { ( b x , b y ) : | b x c n x G ( l B + 1 2 ) | G 2 ~and~ | b y c n y G ( k B + 1 2 ) | G 2 }   ,
g n ( l , k ) = { 1 , if  c b n ( l , k )  for any  c O n . 0 , otherwise .
S ( g 1 , g 2 ) = 2 | | g 1   A N D   g 2 | | 0 ( d i m ( g 1 ) + d i m ( g 2 ) ) ,
S ^ ( g 1 , g 2 ) = | | g 1   A N D   g 2 | | 0 ,
m ^ n = arg max m S ^ ( g n a , g m b ) .
F a b = { ( f n , a a , f n , b a ) : n ~where~ s n a > D } { ( f m , a b , f m , b b ) : m ~where~ s m b > D } ,
F a = T b a F b   ,
F a = [ x 1 , a y 1 , a 1 x 2 , a y 2 , a 1 x P , a y P , a 1 ]
F b = [ x 1 , b y 1 , b 1 x 2 , b y 2 , b 1 x P , b y P , b 1 ]
T b a = L [ cos θ sin θ t x sin θ cos θ t y 0 0 1 ] .
v n r = c r n c n
T n r = [ cos θ sin θ sin θ cos θ ] ,
θ = a t a n 2 ( | | v n r × u | | , v n r u )
O n r = { T n r ( c x c n ) + c n : c x O n }   .
d ¯ = { O n O n r : n { 1 , 2 , N } , r { r 1 n , r 2 n , r R n } }   ,
x ( i , t ) = T ref ( i , t ) ( x ref )   ,
I i , t , m ref ( x ref ) = I i , t , m ( T ref ( i , t ) ( x ref ) )
T ref ( i , 0 ) ( x ref ) = T ( i-1 , 0 ) ( i , 0 ) ( T ( i-2 , 0 ) ( i-1 , 0 ) ( T ( 1 , 0 ) ( 2 , 0 ) ( T ref ( 1 , 0 ) ( x ref ) ) ) )   ,
T ref ( i , t ) ( x ref ) = T ( i , 0 ) ( i , t ) ( T ref ( i , 0 ) ( x ref ) )   .