In Vivo Snapshot Hyper Spectral Image Analysis of Age-Related Macular Degeneration (AMD)
The 32nd Annual International Conference of the IEEE EMBSAugust 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
Ξ Reichert Ophthalmic Instruments Inc., NY USA± Doheny Eye Institute, University of Southern California, CA USA
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OutlineIntroduction - Objective - Background - Related Work - Contribution
ApproachExperimental resultsSummary and conclusion
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Objective
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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).
Background
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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)
Terminology
Background
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Drusen VesselMacula Pigment (MP) (Sharp Vision)
RGB Color Fundus (3 bands) Hyper Spectral Cube (> 50 bands)
Spectral Bands
Show better cube that shows hyperspectral signal
Related Work
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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
Contribution
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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
OutlineIntroduction - Background - Challenges - Contribution
ApproachExperimental resultsSummary and conclusion
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Approach
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Non-Negative Matrix Factorization (NMF)
Basis CoefficientsMatriziced Cube Rankn = # of pixels of single sub-bandm = # of sub-bands in cube
r = rank for dimensionality reduction
On hyper spectral snapshot cube
Cube
Approach
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Constrained optimization problem - Lee & Seung, Sajda et al.
Original Matriziced Cube Frobenius Norm Non-Negativity Constraints
Approach
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NMF Initialization - Physical meaningful spectra to initialize W and H
MP spectrum (In Vitro)Drusen Slices
InitializersCube
OutlineIntroduction - Background - Challenges - Contribution
ApproachExperimental results - Experiment I (Drusen = Disease) - Experiment II (Macular Pigment (MP) = Anatomy)
Summary and conclusion
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Results
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Datasets - 7 patients and 3 controls Controls
MaculaDrusenShown above is patient “c” and 20 ROIs
Results
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Drusen Spectra - Without vs. With ROI stratification using physical meaningful basis
Results
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Macular Pigment (MP) Spectra - With prior initialization using in vitro MP spectra - First to show L + Z peaks in vivo
Conclusion
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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
References
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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.
Acknowledgements
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Thank You
This work was supported by RO1 EY015520 (NIH, NEI) and Research to Prevent Blindness (RPB)
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