1. In Vivo Snapshot Hyper Spectral Image Analysis of Age-Related Macular Degeneration (AMD) The 32 nd 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. Martin Ξ, M. S. Humayun ±, R. T. Smith ‡ †Department of Biomedical Engineering, Columbia University, NY USA ‡ Department of Ophthalmology, Columbia University, NY USA Ξ Reichert Ophthalmic Instruments Inc., NY USA ± Doheny Eye Institute, University of Southern California, CA USA 1

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

3. Objective 3 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 4 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 in vivo drusen imaging + analysis - Hyper spectral imaging can provide spectral information on pigment structure (> 50 spectral bands)

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

6. Related Work 6 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 7 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 8

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

10. Approach 10 Constrained optimization problem - Lee & Seung, Sajda et al. Original Matriziced CubeFrobenius NormNon-Negativity Constraints

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

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

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

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

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

16. Conclusion 16 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 17 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 18 Thank You This work was supported by RO1 EY015520 (NIH, NEI) and Research to Prevent Blindness (RPB)