In Vivo Snapshot Hyper Spectral Image Analysis of Age-Related Macular Degeneration (AMD)

1. In Vivo Snapshot Hyper Spectral Image Analysis of Age-Related Macular Degeneration (AMD) The 32nd Annual International Conference of the IEEE EMBS August 31-September 4, 2010, Buenos Aires, Argentina Noah Lee†, J. Wielaard‡,A. A. Fawzi±, P. Sajda†, A. F. Laine†, G. MartinΞ, M. S. Humayun±, R. T. Smith‡ †Department of Biomedical Engineering, Columbia University, NY USA ‡Department of Ophthalmology, Columbia University, NY USA ΞReichert Ophthalmic Instruments Inc., NY USA ± Doheny Eye Institute, University of Southern California, CA USA

2. Outline • Introduction • - Objective • - Background • - Related Work • - Contribution • Approach • Experimental results • Summary and conclusion

3. Objective • A method for automatic quantification of retinal pigments for disease modeling • - To analyze diseased and normal retinas • - Identify biochemical distributions of retinal pigments (e.g. drusen) • - Simple + rapid + non-invasive • Goal • - Gain understanding into unknown disease process of • AMD (Age related Macular Degeneration).

4. Background • Age-related macular degeneration (AMD) • - Leading cause of blindness in USA • - 5.5 million visually impaired people in 2020 • Drusen are the hallmark of AMD • - Disease process not fully understood • - Biochemical composition is key for understanding AMD • Need for invivo drusen imaging + analysis • - Hyper spectral imaging can provide spectral information on pigment structure (> 50 spectral bands)

5. Background • Terminology RGB Color Fundus (3 bands) Hyper Spectral Cube (> 50 bands) Spectral Bands Macula Pigment (MP) (Sharp Vision) Drusen Vessel Show better cube that shows hyperspectral signal

6. Related Work • In vitro studies dominate the field • - Time consuming • Current spectral imaging limited • - Low # of spectral bands • - Movement artifacts + registration difficulties • Existing analysis methods complicated • - Need to deal with artifacts, mixed sources, noise • - Lack of model interpretability

7. Contribution • Movement artifact free hyper spectral imaging • - Snapshot technology (no moving parts) • - No need to register • - > 50 spectral bands and rapid acquisition • Non-negative matrix factorization • - Parts based representation • - Model: account for reflectivity/absorbance of retinal pigments • - Normalization: account for high dynamic range • - Initialization: physical meaningful priors • The first to show MP with L+Z distribution in vivo • - Bifid Lutein(L) + Zeaxanthin(Z) Peaks (Carotenoid Pigments) • - MP spectra and L + Z peaks in agreement with literature

8. Outline • Introduction • - Background • - Challenges • - Contribution • Approach • Experimental results • Summary and conclusion

9. On hyper spectral snapshot cube Approach Non-Negative Matrix Factorization (NMF) Cube Matriziced Cube Basis Coefficients Rank n = # of pixels of single sub-band m = # of sub-bands in cube r = rank for dimensionality reduction

10. Approach Constrained optimization problem - Lee & Seung, Sajda et al. Original Matriziced Cube Frobenius Norm Non-Negativity Constraints

11. Approach NMF Initialization - Physical meaningful spectra to initialize W and H Initializers Cube Drusen Slices MP spectrum (In Vitro)

12. Outline • Introduction • - Background • - Challenges • - Contribution • Approach • Experimental results • - Experiment I (Drusen = Disease) • - Experiment II (Macular Pigment (MP) = Anatomy) • Summary and conclusion

13. Results Datasets - 7 patients and 3 controls Controls Shown above is patient “c” and 20 ROIs Drusen Macula

14. Results Drusen Spectra - Without vs. With ROI stratification using physical meaningful basis

15. Results Macular Pigment (MP) Spectra - With prior initialization using in vitro MP spectra - First to show L + Z peaks in vivo

16. Conclusion • Snapshot hyper spectral imaging • - High resolution + movement artifact free • - In vivo analysis of spectral fundus pigment distributions • Non-Negative Matrix Factorization • - Need for correct normalization • - Physical meaningful priors as initializers useful • - Obtained reproducible results • Diseased and Anatomical Spectra • - In vivo Drusen + Macular Pigment • - The first to show in vivo bifid Lutein(L) and Zeaxanthin(Z) Peaks

17. References • W. R. Johnson, et al.: Spatial-spectral modulating snapshot hyperspectral imager. Applied Optics, vol 45(9), pp.1898-1908, 2006. • D. Lee and H. Seung: Learning the parts of objects by non-negative matrix factorization. Nature 401, pp.788-791, 1999. • P. Sajda, S. Du, L. Parra: Recovery of constituent spectra using non-negative matrix factorization. Proc. of SPIE, San Diego, CA, pp. 312-331, 2003. • N. Lee, A. Laine, R. T. Smith: A hybrid segmentation approach for geographic atrophy in fundus auto-fluorescence images for diagnosis of age-related macular degeneration. Proc. of IEEE EMBS, pp. 1140-1143, Lyon, France, 2007.

18. Acknowledgements Thank You This work was supported by RO1 EY015520 (NIH, NEI) and Research to Prevent Blindness (RPB)