Abstract

Precise measurements of photoreceptor numerosity and spatial arrangement are promising biomarkers for the early detection of retinal pathologies and may be valuable in the evaluation of retinal therapies. Adaptive optics scanning light ophthalmoscopy (AOSLO) is a method of imaging that corrects for aberrations of the eye to acquire high-resolution images that reveal the photoreceptor mosaic. These images are typically graded manually by experienced observers, obviating the robust, large-scale use of the technology. This paper addresses unsupervised automated detection of cones in non-confocal, split-detection AOSLO images. Our algorithm leverages the appearance of split-detection images to create a cone model that is used for classification. Results show that it compares favorably to the state-of-the-art, both for images of healthy retinas and for images from patients affected by Stargardt disease. The algorithm presented also compares well to manual annotation while excelling in speed.

© 2017 Optical Society of America

Full Article  |  PDF Article
OSA Recommended Articles
Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images

David Cunefare, Robert F. Cooper, Brian Higgins, David F. Katz, Alfredo Dubra, Joseph Carroll, and Sina Farsiu
Biomed. Opt. Express 7(5) 2036-2050 (2016)

Automatic cone photoreceptor segmentation using graph theory and dynamic programming

Stephanie J. Chiu, Yuliya Lokhnygina, Adam M. Dubis, Alfredo Dubra, Joseph Carroll, Joseph A. Izatt, and Sina Farsiu
Biomed. Opt. Express 4(6) 924-937 (2013)

Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks

Freerk G. Venhuizen, Bram van Ginneken, Bart Liefers, Mark J.J.P. van Grinsven, Sascha Fauser, Carel Hoyng, Thomas Theelen, and Clara I. Sánchez
Biomed. Opt. Express 8(7) 3292-3316 (2017)

References

  • View by:
  • |
  • |
  • |

  1. A. Roorda and J. L. Duncan, “Adaptive optics ophthalmoscopy,” Annu. Rev. Vis. Sci. 1, 19–50 (2015).
    [Crossref]
  2. T. Y. Chui, H. Song, and S. A. Burns, “Adaptive-optics imaging of human cone photoreceptor distribution,” J. Opt. Soc. Am. A 25, 3021–3029 (2008).
    [Crossref]
  3. M. Lombardo, S. Serrao, N. Devaney, M. Parravano, and G. Lombardo, “Adaptive optics technology for high-resolution retinal imaging,” Sensors 13, 334–366 (2012).
    [Crossref] [PubMed]
  4. T. Y. P. Chui, H. Song, and S. A. Burns, “Individual variations in human cone photoreceptor packing density: variations with refractive error,” Invest. Ophth. Vis. Sci. 49, 4679–4687 (2008).
    [Crossref]
  5. R. F. Cooper, M. Lombardo, J. Carroll, K. R. Sloan, and G. Lombardo, “Methods for investigating the local spatial anisotropy and the preferred orientation of cones in adaptive optics retinal images,” Visual Neurosci. 33, E005 (2016).
    [Crossref]
  6. R. F. Cooper, M. A. Wilk, S. Tarima, and J. Carroll, “Evaluating descriptive metrics of the human cone mosaic,” Invest. Ophth. Vis. Sci. 57, 2992–3001 (2016).
    [Crossref]
  7. K. Y. Li and A. Roorda, “Automated identification of cone photoreceptors in adaptive optics retinal images,” J. Opt. Soc. Am. A 24, 1358–1363 (2007).
    [Crossref]
  8. L. Mariotti and N. Devaney, “Performance analysis of cone detection algorithms,” J. Opt. Soc. Am. A 32, 497–506 (2015).
    [Crossref]
  9. D. M. Bukowska, A. L. Chew, E. Huynh, I. Kashani, S. L. Wan, P. M. Wan, and F. K. Chen, “Semi-automated identification of cones in the human retina using circle Hough transform,” Biomed. Opt. Express 6, 4676 (2015).
    [Crossref] [PubMed]
  10. S. J. Chiu, Y. Lokhnygina, A. M. Dubis, A. Dubra, J. Carroll, J. A. Izatt, and S. Farsiu, “Automatic cone photoreceptor segmentation using graph theory and dynamic programming,” Biomed. Opt. Express 4, 924–937 (2013).
    [Crossref] [PubMed]
  11. L. Mariotti and N. Devaney, “Cone detection and blood vessel segmentation on AO retinal images,” in Proceedings of Irish Machine Vision and Image Processing pp. 126–128 (2015).
  12. D. Scoles, Y. N. Sulai, C. S. Langlo, G. Fishman, C. Curcio, J. Carroll, and A. Dubra, “In vivo imaging of human cone photoreceptor inner segments,” Invest. Ophth. Vis. Sci. 55, 4244–4251 (2014).
    [Crossref]
  13. E. A. Rossi, C. E. Granger, R. Sharma, Q. Yang, K. Saito, C. Schwarz, S. Walters, K. Nozato, J. Zhang, T. Kawakami, and et al., “Imaging individual neurons in the retinal ganglion cell layer of the living eye,” Proc. Natl. Acad. Sci. U.S.A. p. 201613445 (2017).
  14. A. Lucchi, K. Smith, R. Achanta, V. Lepetit, and P. Fua, “A fully automated approach to segmentation of irregularly shaped cellular structures in EM images,” in Proceedings Med. Image Comput. Comput. Assist. Interv. pp. 463–471 (2010).
  15. S. Dimopoulos, C. E. Mayer, F. Rudolf, and J. Stelling, “Accurate cell segmentation in microscopy images using membrane patterns,” Bioinformatics 30, 2644–2651 (2014).
    [Crossref] [PubMed]
  16. J. Liu, A. Dubra, and J. Tam, “Computer-aided detection of human cone photoreceptor inner segments using multi-scale circular voting,” Proc. SPIE Medical Imaging pp. 97851 (2016).
  17. J. Liu, A. Dubra, and J. Tam, “A fully automatic framework for cell segmentation on non-confocal adaptive optics images,” Proc. SPIE Medical Imaging9785(2016).
  18. D. Cunefare, R. F. Cooper, B. Higgins, D. F. Katz, A. Dubra, J. Carroll, and S. Farsiu, “Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 7, 2036–2050 (2016).
    [Crossref] [PubMed]
  19. A. Dubra, Y. Sulai, J. L. Norris, R. F. Cooper, A. M. Dubis, D. R. Williams, and J. Carroll, “Noninvasive imaging of the human rod photoreceptor mosaic using a confocal adaptive optics scanning ophthalmoscope,” Biomed. Opt. Express 2, 1864–1876 (2011).
    [Crossref] [PubMed]
  20. R. F. Cooper, C. S. Langlo, A. Dubra, and J. Carroll, “Automatic detection of modal spacing (yellott’s ring) in adaptive optics scanning light ophthalmoscope images,” Ophthalmic Physiol. Opt. 33, 540–549 (2013).
    [Crossref] [PubMed]
  21. C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proceedings of IEEE International Conference on Computer Vision pp. 839–846 (1998).
  22. J. B. Zimmerman, S. M. Pizer, E. V. Staab, J. R. Perry, W. McCartney, and B. C. Brenton, “Evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement,” IEEE Trans. Med. Imaging 7, 304–312 (1988).
    [Crossref]
  23. N. Otsu, “A threshold-selection method from gray-level histograms,” IEEE Trans. Syst. Man. Cybern. 9, 62–66 (1979).
    [Crossref]
  24. C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Learning to detect cells using non-overlapping extremal regions,” in Proceedings Med. Image Comput. Comput. Assist. Interv. pp. 348–356 (2012).

2016 (3)

R. F. Cooper, M. Lombardo, J. Carroll, K. R. Sloan, and G. Lombardo, “Methods for investigating the local spatial anisotropy and the preferred orientation of cones in adaptive optics retinal images,” Visual Neurosci. 33, E005 (2016).
[Crossref]

R. F. Cooper, M. A. Wilk, S. Tarima, and J. Carroll, “Evaluating descriptive metrics of the human cone mosaic,” Invest. Ophth. Vis. Sci. 57, 2992–3001 (2016).
[Crossref]

D. Cunefare, R. F. Cooper, B. Higgins, D. F. Katz, A. Dubra, J. Carroll, and S. Farsiu, “Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 7, 2036–2050 (2016).
[Crossref] [PubMed]

2015 (3)

2014 (2)

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

S. Dimopoulos, C. E. Mayer, F. Rudolf, and J. Stelling, “Accurate cell segmentation in microscopy images using membrane patterns,” Bioinformatics 30, 2644–2651 (2014).
[Crossref] [PubMed]

2013 (2)

R. F. Cooper, C. S. Langlo, A. Dubra, and J. Carroll, “Automatic detection of modal spacing (yellott’s ring) in adaptive optics scanning light ophthalmoscope images,” Ophthalmic Physiol. Opt. 33, 540–549 (2013).
[Crossref] [PubMed]

S. J. Chiu, Y. Lokhnygina, A. M. Dubis, A. Dubra, J. Carroll, J. A. Izatt, and S. Farsiu, “Automatic cone photoreceptor segmentation using graph theory and dynamic programming,” Biomed. Opt. Express 4, 924–937 (2013).
[Crossref] [PubMed]

2012 (1)

M. Lombardo, S. Serrao, N. Devaney, M. Parravano, and G. Lombardo, “Adaptive optics technology for high-resolution retinal imaging,” Sensors 13, 334–366 (2012).
[Crossref] [PubMed]

2011 (1)

2008 (2)

T. Y. P. Chui, H. Song, and S. A. Burns, “Individual variations in human cone photoreceptor packing density: variations with refractive error,” Invest. Ophth. Vis. Sci. 49, 4679–4687 (2008).
[Crossref]

T. Y. Chui, H. Song, and S. A. Burns, “Adaptive-optics imaging of human cone photoreceptor distribution,” J. Opt. Soc. Am. A 25, 3021–3029 (2008).
[Crossref]

2007 (1)

1988 (1)

J. B. Zimmerman, S. M. Pizer, E. V. Staab, J. R. Perry, W. McCartney, and B. C. Brenton, “Evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement,” IEEE Trans. Med. Imaging 7, 304–312 (1988).
[Crossref]

1979 (1)

N. Otsu, “A threshold-selection method from gray-level histograms,” IEEE Trans. Syst. Man. Cybern. 9, 62–66 (1979).
[Crossref]

Achanta, R.

A. Lucchi, K. Smith, R. Achanta, V. Lepetit, and P. Fua, “A fully automated approach to segmentation of irregularly shaped cellular structures in EM images,” in Proceedings Med. Image Comput. Comput. Assist. Interv. pp. 463–471 (2010).

Arteta, C.

C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Learning to detect cells using non-overlapping extremal regions,” in Proceedings Med. Image Comput. Comput. Assist. Interv. pp. 348–356 (2012).

Brenton, B. C.

J. B. Zimmerman, S. M. Pizer, E. V. Staab, J. R. Perry, W. McCartney, and B. C. Brenton, “Evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement,” IEEE Trans. Med. Imaging 7, 304–312 (1988).
[Crossref]

Bukowska, D. M.

Burns, S. A.

T. Y. Chui, H. Song, and S. A. Burns, “Adaptive-optics imaging of human cone photoreceptor distribution,” J. Opt. Soc. Am. A 25, 3021–3029 (2008).
[Crossref]

T. Y. P. Chui, H. Song, and S. A. Burns, “Individual variations in human cone photoreceptor packing density: variations with refractive error,” Invest. Ophth. Vis. Sci. 49, 4679–4687 (2008).
[Crossref]

Carroll, J.

R. F. Cooper, M. Lombardo, J. Carroll, K. R. Sloan, and G. Lombardo, “Methods for investigating the local spatial anisotropy and the preferred orientation of cones in adaptive optics retinal images,” Visual Neurosci. 33, E005 (2016).
[Crossref]

R. F. Cooper, M. A. Wilk, S. Tarima, and J. Carroll, “Evaluating descriptive metrics of the human cone mosaic,” Invest. Ophth. Vis. Sci. 57, 2992–3001 (2016).
[Crossref]

D. Cunefare, R. F. Cooper, B. Higgins, D. F. Katz, A. Dubra, J. Carroll, and S. Farsiu, “Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 7, 2036–2050 (2016).
[Crossref] [PubMed]

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

R. F. Cooper, C. S. Langlo, A. Dubra, and J. Carroll, “Automatic detection of modal spacing (yellott’s ring) in adaptive optics scanning light ophthalmoscope images,” Ophthalmic Physiol. Opt. 33, 540–549 (2013).
[Crossref] [PubMed]

S. J. Chiu, Y. Lokhnygina, A. M. Dubis, A. Dubra, J. Carroll, J. A. Izatt, and S. Farsiu, “Automatic cone photoreceptor segmentation using graph theory and dynamic programming,” Biomed. Opt. Express 4, 924–937 (2013).
[Crossref] [PubMed]

A. Dubra, Y. Sulai, J. L. Norris, R. F. Cooper, A. M. Dubis, D. R. Williams, and J. Carroll, “Noninvasive imaging of the human rod photoreceptor mosaic using a confocal adaptive optics scanning ophthalmoscope,” Biomed. Opt. Express 2, 1864–1876 (2011).
[Crossref] [PubMed]

Chen, F. K.

Chew, A. L.

Chiu, S. J.

Chui, T. Y.

Chui, T. Y. P.

T. Y. P. Chui, H. Song, and S. A. Burns, “Individual variations in human cone photoreceptor packing density: variations with refractive error,” Invest. Ophth. Vis. Sci. 49, 4679–4687 (2008).
[Crossref]

Cooper, R. F.

R. F. Cooper, M. Lombardo, J. Carroll, K. R. Sloan, and G. Lombardo, “Methods for investigating the local spatial anisotropy and the preferred orientation of cones in adaptive optics retinal images,” Visual Neurosci. 33, E005 (2016).
[Crossref]

R. F. Cooper, M. A. Wilk, S. Tarima, and J. Carroll, “Evaluating descriptive metrics of the human cone mosaic,” Invest. Ophth. Vis. Sci. 57, 2992–3001 (2016).
[Crossref]

D. Cunefare, R. F. Cooper, B. Higgins, D. F. Katz, A. Dubra, J. Carroll, and S. Farsiu, “Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 7, 2036–2050 (2016).
[Crossref] [PubMed]

R. F. Cooper, C. S. Langlo, A. Dubra, and J. Carroll, “Automatic detection of modal spacing (yellott’s ring) in adaptive optics scanning light ophthalmoscope images,” Ophthalmic Physiol. Opt. 33, 540–549 (2013).
[Crossref] [PubMed]

A. Dubra, Y. Sulai, J. L. Norris, R. F. Cooper, A. M. Dubis, D. R. Williams, and J. Carroll, “Noninvasive imaging of the human rod photoreceptor mosaic using a confocal adaptive optics scanning ophthalmoscope,” Biomed. Opt. Express 2, 1864–1876 (2011).
[Crossref] [PubMed]

Cunefare, D.

Curcio, C.

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

Devaney, N.

L. Mariotti and N. Devaney, “Performance analysis of cone detection algorithms,” J. Opt. Soc. Am. A 32, 497–506 (2015).
[Crossref]

M. Lombardo, S. Serrao, N. Devaney, M. Parravano, and G. Lombardo, “Adaptive optics technology for high-resolution retinal imaging,” Sensors 13, 334–366 (2012).
[Crossref] [PubMed]

L. Mariotti and N. Devaney, “Cone detection and blood vessel segmentation on AO retinal images,” in Proceedings of Irish Machine Vision and Image Processing pp. 126–128 (2015).

Dimopoulos, S.

S. Dimopoulos, C. E. Mayer, F. Rudolf, and J. Stelling, “Accurate cell segmentation in microscopy images using membrane patterns,” Bioinformatics 30, 2644–2651 (2014).
[Crossref] [PubMed]

Dubis, A. M.

Dubra, A.

D. Cunefare, R. F. Cooper, B. Higgins, D. F. Katz, A. Dubra, J. Carroll, and S. Farsiu, “Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 7, 2036–2050 (2016).
[Crossref] [PubMed]

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

R. F. Cooper, C. S. Langlo, A. Dubra, and J. Carroll, “Automatic detection of modal spacing (yellott’s ring) in adaptive optics scanning light ophthalmoscope images,” Ophthalmic Physiol. Opt. 33, 540–549 (2013).
[Crossref] [PubMed]

S. J. Chiu, Y. Lokhnygina, A. M. Dubis, A. Dubra, J. Carroll, J. A. Izatt, and S. Farsiu, “Automatic cone photoreceptor segmentation using graph theory and dynamic programming,” Biomed. Opt. Express 4, 924–937 (2013).
[Crossref] [PubMed]

A. Dubra, Y. Sulai, J. L. Norris, R. F. Cooper, A. M. Dubis, D. R. Williams, and J. Carroll, “Noninvasive imaging of the human rod photoreceptor mosaic using a confocal adaptive optics scanning ophthalmoscope,” Biomed. Opt. Express 2, 1864–1876 (2011).
[Crossref] [PubMed]

J. Liu, A. Dubra, and J. Tam, “Computer-aided detection of human cone photoreceptor inner segments using multi-scale circular voting,” Proc. SPIE Medical Imaging pp. 97851 (2016).

J. Liu, A. Dubra, and J. Tam, “A fully automatic framework for cell segmentation on non-confocal adaptive optics images,” Proc. SPIE Medical Imaging9785(2016).

Duncan, J. L.

A. Roorda and J. L. Duncan, “Adaptive optics ophthalmoscopy,” Annu. Rev. Vis. Sci. 1, 19–50 (2015).
[Crossref]

Farsiu, S.

Fishman, G.

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

Fua, P.

A. Lucchi, K. Smith, R. Achanta, V. Lepetit, and P. Fua, “A fully automated approach to segmentation of irregularly shaped cellular structures in EM images,” in Proceedings Med. Image Comput. Comput. Assist. Interv. pp. 463–471 (2010).

Granger, C. E.

E. A. Rossi, C. E. Granger, R. Sharma, Q. Yang, K. Saito, C. Schwarz, S. Walters, K. Nozato, J. Zhang, T. Kawakami, and et al., “Imaging individual neurons in the retinal ganglion cell layer of the living eye,” Proc. Natl. Acad. Sci. U.S.A. p. 201613445 (2017).

Higgins, B.

Huynh, E.

Izatt, J. A.

Kashani, I.

Katz, D. F.

Kawakami, T.

E. A. Rossi, C. E. Granger, R. Sharma, Q. Yang, K. Saito, C. Schwarz, S. Walters, K. Nozato, J. Zhang, T. Kawakami, and et al., “Imaging individual neurons in the retinal ganglion cell layer of the living eye,” Proc. Natl. Acad. Sci. U.S.A. p. 201613445 (2017).

Langlo, C. S.

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

R. F. Cooper, C. S. Langlo, A. Dubra, and J. Carroll, “Automatic detection of modal spacing (yellott’s ring) in adaptive optics scanning light ophthalmoscope images,” Ophthalmic Physiol. Opt. 33, 540–549 (2013).
[Crossref] [PubMed]

Lempitsky, V.

C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Learning to detect cells using non-overlapping extremal regions,” in Proceedings Med. Image Comput. Comput. Assist. Interv. pp. 348–356 (2012).

Lepetit, V.

A. Lucchi, K. Smith, R. Achanta, V. Lepetit, and P. Fua, “A fully automated approach to segmentation of irregularly shaped cellular structures in EM images,” in Proceedings Med. Image Comput. Comput. Assist. Interv. pp. 463–471 (2010).

Li, K. Y.

Liu, J.

J. Liu, A. Dubra, and J. Tam, “A fully automatic framework for cell segmentation on non-confocal adaptive optics images,” Proc. SPIE Medical Imaging9785(2016).

J. Liu, A. Dubra, and J. Tam, “Computer-aided detection of human cone photoreceptor inner segments using multi-scale circular voting,” Proc. SPIE Medical Imaging pp. 97851 (2016).

Lokhnygina, Y.

Lombardo, G.

R. F. Cooper, M. Lombardo, J. Carroll, K. R. Sloan, and G. Lombardo, “Methods for investigating the local spatial anisotropy and the preferred orientation of cones in adaptive optics retinal images,” Visual Neurosci. 33, E005 (2016).
[Crossref]

M. Lombardo, S. Serrao, N. Devaney, M. Parravano, and G. Lombardo, “Adaptive optics technology for high-resolution retinal imaging,” Sensors 13, 334–366 (2012).
[Crossref] [PubMed]

Lombardo, M.

R. F. Cooper, M. Lombardo, J. Carroll, K. R. Sloan, and G. Lombardo, “Methods for investigating the local spatial anisotropy and the preferred orientation of cones in adaptive optics retinal images,” Visual Neurosci. 33, E005 (2016).
[Crossref]

M. Lombardo, S. Serrao, N. Devaney, M. Parravano, and G. Lombardo, “Adaptive optics technology for high-resolution retinal imaging,” Sensors 13, 334–366 (2012).
[Crossref] [PubMed]

Lucchi, A.

A. Lucchi, K. Smith, R. Achanta, V. Lepetit, and P. Fua, “A fully automated approach to segmentation of irregularly shaped cellular structures in EM images,” in Proceedings Med. Image Comput. Comput. Assist. Interv. pp. 463–471 (2010).

Manduchi, R.

C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proceedings of IEEE International Conference on Computer Vision pp. 839–846 (1998).

Mariotti, L.

L. Mariotti and N. Devaney, “Performance analysis of cone detection algorithms,” J. Opt. Soc. Am. A 32, 497–506 (2015).
[Crossref]

L. Mariotti and N. Devaney, “Cone detection and blood vessel segmentation on AO retinal images,” in Proceedings of Irish Machine Vision and Image Processing pp. 126–128 (2015).

Mayer, C. E.

S. Dimopoulos, C. E. Mayer, F. Rudolf, and J. Stelling, “Accurate cell segmentation in microscopy images using membrane patterns,” Bioinformatics 30, 2644–2651 (2014).
[Crossref] [PubMed]

McCartney, W.

J. B. Zimmerman, S. M. Pizer, E. V. Staab, J. R. Perry, W. McCartney, and B. C. Brenton, “Evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement,” IEEE Trans. Med. Imaging 7, 304–312 (1988).
[Crossref]

Noble, J. A.

C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Learning to detect cells using non-overlapping extremal regions,” in Proceedings Med. Image Comput. Comput. Assist. Interv. pp. 348–356 (2012).

Norris, J. L.

Nozato, K.

E. A. Rossi, C. E. Granger, R. Sharma, Q. Yang, K. Saito, C. Schwarz, S. Walters, K. Nozato, J. Zhang, T. Kawakami, and et al., “Imaging individual neurons in the retinal ganglion cell layer of the living eye,” Proc. Natl. Acad. Sci. U.S.A. p. 201613445 (2017).

Otsu, N.

N. Otsu, “A threshold-selection method from gray-level histograms,” IEEE Trans. Syst. Man. Cybern. 9, 62–66 (1979).
[Crossref]

Parravano, M.

M. Lombardo, S. Serrao, N. Devaney, M. Parravano, and G. Lombardo, “Adaptive optics technology for high-resolution retinal imaging,” Sensors 13, 334–366 (2012).
[Crossref] [PubMed]

Perry, J. R.

J. B. Zimmerman, S. M. Pizer, E. V. Staab, J. R. Perry, W. McCartney, and B. C. Brenton, “Evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement,” IEEE Trans. Med. Imaging 7, 304–312 (1988).
[Crossref]

Pizer, S. M.

J. B. Zimmerman, S. M. Pizer, E. V. Staab, J. R. Perry, W. McCartney, and B. C. Brenton, “Evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement,” IEEE Trans. Med. Imaging 7, 304–312 (1988).
[Crossref]

Roorda, A.

Rossi, E. A.

E. A. Rossi, C. E. Granger, R. Sharma, Q. Yang, K. Saito, C. Schwarz, S. Walters, K. Nozato, J. Zhang, T. Kawakami, and et al., “Imaging individual neurons in the retinal ganglion cell layer of the living eye,” Proc. Natl. Acad. Sci. U.S.A. p. 201613445 (2017).

Rudolf, F.

S. Dimopoulos, C. E. Mayer, F. Rudolf, and J. Stelling, “Accurate cell segmentation in microscopy images using membrane patterns,” Bioinformatics 30, 2644–2651 (2014).
[Crossref] [PubMed]

Saito, K.

E. A. Rossi, C. E. Granger, R. Sharma, Q. Yang, K. Saito, C. Schwarz, S. Walters, K. Nozato, J. Zhang, T. Kawakami, and et al., “Imaging individual neurons in the retinal ganglion cell layer of the living eye,” Proc. Natl. Acad. Sci. U.S.A. p. 201613445 (2017).

Schwarz, C.

E. A. Rossi, C. E. Granger, R. Sharma, Q. Yang, K. Saito, C. Schwarz, S. Walters, K. Nozato, J. Zhang, T. Kawakami, and et al., “Imaging individual neurons in the retinal ganglion cell layer of the living eye,” Proc. Natl. Acad. Sci. U.S.A. p. 201613445 (2017).

Scoles, D.

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

Serrao, S.

M. Lombardo, S. Serrao, N. Devaney, M. Parravano, and G. Lombardo, “Adaptive optics technology for high-resolution retinal imaging,” Sensors 13, 334–366 (2012).
[Crossref] [PubMed]

Sharma, R.

E. A. Rossi, C. E. Granger, R. Sharma, Q. Yang, K. Saito, C. Schwarz, S. Walters, K. Nozato, J. Zhang, T. Kawakami, and et al., “Imaging individual neurons in the retinal ganglion cell layer of the living eye,” Proc. Natl. Acad. Sci. U.S.A. p. 201613445 (2017).

Sloan, K. R.

R. F. Cooper, M. Lombardo, J. Carroll, K. R. Sloan, and G. Lombardo, “Methods for investigating the local spatial anisotropy and the preferred orientation of cones in adaptive optics retinal images,” Visual Neurosci. 33, E005 (2016).
[Crossref]

Smith, K.

A. Lucchi, K. Smith, R. Achanta, V. Lepetit, and P. Fua, “A fully automated approach to segmentation of irregularly shaped cellular structures in EM images,” in Proceedings Med. Image Comput. Comput. Assist. Interv. pp. 463–471 (2010).

Song, H.

T. Y. P. Chui, H. Song, and S. A. Burns, “Individual variations in human cone photoreceptor packing density: variations with refractive error,” Invest. Ophth. Vis. Sci. 49, 4679–4687 (2008).
[Crossref]

T. Y. Chui, H. Song, and S. A. Burns, “Adaptive-optics imaging of human cone photoreceptor distribution,” J. Opt. Soc. Am. A 25, 3021–3029 (2008).
[Crossref]

Staab, E. V.

J. B. Zimmerman, S. M. Pizer, E. V. Staab, J. R. Perry, W. McCartney, and B. C. Brenton, “Evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement,” IEEE Trans. Med. Imaging 7, 304–312 (1988).
[Crossref]

Stelling, J.

S. Dimopoulos, C. E. Mayer, F. Rudolf, and J. Stelling, “Accurate cell segmentation in microscopy images using membrane patterns,” Bioinformatics 30, 2644–2651 (2014).
[Crossref] [PubMed]

Sulai, Y.

Sulai, Y. N.

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

Tam, J.

J. Liu, A. Dubra, and J. Tam, “A fully automatic framework for cell segmentation on non-confocal adaptive optics images,” Proc. SPIE Medical Imaging9785(2016).

J. Liu, A. Dubra, and J. Tam, “Computer-aided detection of human cone photoreceptor inner segments using multi-scale circular voting,” Proc. SPIE Medical Imaging pp. 97851 (2016).

Tarima, S.

R. F. Cooper, M. A. Wilk, S. Tarima, and J. Carroll, “Evaluating descriptive metrics of the human cone mosaic,” Invest. Ophth. Vis. Sci. 57, 2992–3001 (2016).
[Crossref]

Tomasi, C.

C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proceedings of IEEE International Conference on Computer Vision pp. 839–846 (1998).

Walters, S.

E. A. Rossi, C. E. Granger, R. Sharma, Q. Yang, K. Saito, C. Schwarz, S. Walters, K. Nozato, J. Zhang, T. Kawakami, and et al., “Imaging individual neurons in the retinal ganglion cell layer of the living eye,” Proc. Natl. Acad. Sci. U.S.A. p. 201613445 (2017).

Wan, P. M.

Wan, S. L.

Wilk, M. A.

R. F. Cooper, M. A. Wilk, S. Tarima, and J. Carroll, “Evaluating descriptive metrics of the human cone mosaic,” Invest. Ophth. Vis. Sci. 57, 2992–3001 (2016).
[Crossref]

Williams, D. R.

Yang, Q.

E. A. Rossi, C. E. Granger, R. Sharma, Q. Yang, K. Saito, C. Schwarz, S. Walters, K. Nozato, J. Zhang, T. Kawakami, and et al., “Imaging individual neurons in the retinal ganglion cell layer of the living eye,” Proc. Natl. Acad. Sci. U.S.A. p. 201613445 (2017).

Zhang, J.

E. A. Rossi, C. E. Granger, R. Sharma, Q. Yang, K. Saito, C. Schwarz, S. Walters, K. Nozato, J. Zhang, T. Kawakami, and et al., “Imaging individual neurons in the retinal ganglion cell layer of the living eye,” Proc. Natl. Acad. Sci. U.S.A. p. 201613445 (2017).

Zimmerman, J. B.

J. B. Zimmerman, S. M. Pizer, E. V. Staab, J. R. Perry, W. McCartney, and B. C. Brenton, “Evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement,” IEEE Trans. Med. Imaging 7, 304–312 (1988).
[Crossref]

Zisserman, A.

C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Learning to detect cells using non-overlapping extremal regions,” in Proceedings Med. Image Comput. Comput. Assist. Interv. pp. 348–356 (2012).

Annu. Rev. Vis. Sci. (1)

A. Roorda and J. L. Duncan, “Adaptive optics ophthalmoscopy,” Annu. Rev. Vis. Sci. 1, 19–50 (2015).
[Crossref]

Bioinformatics (1)

S. Dimopoulos, C. E. Mayer, F. Rudolf, and J. Stelling, “Accurate cell segmentation in microscopy images using membrane patterns,” Bioinformatics 30, 2644–2651 (2014).
[Crossref] [PubMed]

Biomed. Opt. Express (4)

IEEE Trans. Med. Imaging (1)

J. B. Zimmerman, S. M. Pizer, E. V. Staab, J. R. Perry, W. McCartney, and B. C. Brenton, “Evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement,” IEEE Trans. Med. Imaging 7, 304–312 (1988).
[Crossref]

IEEE Trans. Syst. Man. Cybern. (1)

N. Otsu, “A threshold-selection method from gray-level histograms,” IEEE Trans. Syst. Man. Cybern. 9, 62–66 (1979).
[Crossref]

Invest. Ophth. Vis. Sci. (3)

R. F. Cooper, M. A. Wilk, S. Tarima, and J. Carroll, “Evaluating descriptive metrics of the human cone mosaic,” Invest. Ophth. Vis. Sci. 57, 2992–3001 (2016).
[Crossref]

T. Y. P. Chui, H. Song, and S. A. Burns, “Individual variations in human cone photoreceptor packing density: variations with refractive error,” Invest. Ophth. Vis. Sci. 49, 4679–4687 (2008).
[Crossref]

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

J. Opt. Soc. Am. A (3)

Ophthalmic Physiol. Opt. (1)

R. F. Cooper, C. S. Langlo, A. Dubra, and J. Carroll, “Automatic detection of modal spacing (yellott’s ring) in adaptive optics scanning light ophthalmoscope images,” Ophthalmic Physiol. Opt. 33, 540–549 (2013).
[Crossref] [PubMed]

Sensors (1)

M. Lombardo, S. Serrao, N. Devaney, M. Parravano, and G. Lombardo, “Adaptive optics technology for high-resolution retinal imaging,” Sensors 13, 334–366 (2012).
[Crossref] [PubMed]

Visual Neurosci. (1)

R. F. Cooper, M. Lombardo, J. Carroll, K. R. Sloan, and G. Lombardo, “Methods for investigating the local spatial anisotropy and the preferred orientation of cones in adaptive optics retinal images,” Visual Neurosci. 33, E005 (2016).
[Crossref]

Other (7)

L. Mariotti and N. Devaney, “Cone detection and blood vessel segmentation on AO retinal images,” in Proceedings of Irish Machine Vision and Image Processing pp. 126–128 (2015).

C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proceedings of IEEE International Conference on Computer Vision pp. 839–846 (1998).

E. A. Rossi, C. E. Granger, R. Sharma, Q. Yang, K. Saito, C. Schwarz, S. Walters, K. Nozato, J. Zhang, T. Kawakami, and et al., “Imaging individual neurons in the retinal ganglion cell layer of the living eye,” Proc. Natl. Acad. Sci. U.S.A. p. 201613445 (2017).

A. Lucchi, K. Smith, R. Achanta, V. Lepetit, and P. Fua, “A fully automated approach to segmentation of irregularly shaped cellular structures in EM images,” in Proceedings Med. Image Comput. Comput. Assist. Interv. pp. 463–471 (2010).

J. Liu, A. Dubra, and J. Tam, “Computer-aided detection of human cone photoreceptor inner segments using multi-scale circular voting,” Proc. SPIE Medical Imaging pp. 97851 (2016).

J. Liu, A. Dubra, and J. Tam, “A fully automatic framework for cell segmentation on non-confocal adaptive optics images,” Proc. SPIE Medical Imaging9785(2016).

C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman, “Learning to detect cells using non-overlapping extremal regions,” in Proceedings Med. Image Comput. Comput. Assist. Interv. pp. 348–356 (2012).

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (6)

Fig. 1
Fig. 1

(a) Illustration of the level of detail provided by AOSLO imaging. (b) Comparison of the confocal imaging approach to split-detection: (left) confocal imaging of cones, and (right) split-detection imaging of cones. The two images are matching and acquired in perfect registration. The annotated cones exemplify their different appearance on the two modalities.

Fig. 2
Fig. 2

Algorithm workflow on a representative non-confocal AO image from our Dataset.

Fig. 3
Fig. 3

(a) The non-overlapping extremal region detection algorithm. (b) Components of the feature vector for a cone. Normalization is performed for illustration purposes.

Fig. 4
Fig. 4

Example healthy images from the AOSLO (top: raw, bottom: processed and annotated). Green circles: matched detections. Yellow triangles: unmatched human detections. Red triangles: unmatched algorithm detections.

Fig. 5
Fig. 5

Example Stargardt images from the AOSLO (top: raw, bottom: processed and annotated). Green circles: matched detections. Yellow triangles: unmatched human detections. Red triangles: unmatched algorithm detections.

Fig. 6
Fig. 6

Performance metrics for images of healthy retinas [(a), (c), (d)], and images of retinas affected by Stargardt disease [(b), (d), (f)]. First row: DICE coefficient; Second row: Precision metric; Third row: Recall metric.

Tables (2)

Tables Icon

Table 1 Parameters of the algorithm: fixed within the datasets.

Tables Icon

Table 2 Contribution of each classification step.

Equations (5)

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

I AO , S = I R I D I R + I D .
I acc ( i , j ) = x = i d w i + d w y = j d w j + d w I thresh ( x , y ) , where I thresh ( x , y ) = { 0 if I w ( x , y ) T ( W ) 1 if otherwise
f v = [ f ao , polar f ao , g , polar f ao , diff f ao , rot- 90 f ao , rot- 180 ] 1260 × 1
label = { cone , if f v U ( U T f V ) 2 e thresh , cone background , if f v U ( U T f V ) 2 e thresh , background
DICE = 2 × ( # P m ) ( # P 1 ) + ( # P 2 )

Metrics