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)