In Vivo Snapshot Hyper Spectral Image Analysis of Age-Related Macular Degeneration (AMD) The 32 nd...

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  • Slide 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 , M. S. Humayun , R. T. Smith Department of Biomedical Engineering, Columbia University, NY USA Department of Ophthalmology, Columbia University, NY USA Reichert Ophthalmic Instruments Inc., NY USA Doheny Eye Institute, University of Southern California, CA USA 1
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  • Outline Introduction - Objective - Background - Related Work - Contribution Approach Experimental results Summary and conclusion 2
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  • 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).
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  • 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)
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  • 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
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  • 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
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  • 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
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  • Outline Introduction - Background - Challenges - Contribution Approach Experimental results Summary and conclusion 8
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  • 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
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  • Approach 10 Constrained optimization problem - Lee & Seung, Sajda et al. Original Matriziced CubeFrobenius NormNon-Negativity Constraints
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  • Approach 11 NMF Initialization - Physical meaningful spectra to initialize W and H MP spectrum (In Vitro)Drusen Slices Initializers Cube
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  • Outline Introduction - Background - Challenges - Contribution Approach Experimental results - Experiment I (Drusen = Disease) - Experiment II (Macular Pigment (MP) = Anatomy) Summary and conclusion 12
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  • Results 13 Datasets - 7 patients and 3 controls Controls Macula Drusen Shown above is patient c and 20 ROIs
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  • Results 14 Drusen Spectra - Without vs. With ROI stratification using physical meaningful basis
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  • Results 15 Macular Pigment (MP) Spectra - With prior initialization using in vitro MP spectra - First to show L + Z peaks in vivo
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  • 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
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  • 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.
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  • Acknowledgements 18 Thank You This work was supported by RO1 EY015520 (NIH, NEI) and Research to Prevent Blindness (RPB)