Prof. Gustave L. Davis Profile

Prof. Gustave L. Davis

Pathology Professor at Yale School of Medicine

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Proceedings Article | 23 February 2006 Paper

Hyperspectral microscopic analysis of normal, benign and carcinoma microarray tissue sections

Mauro Maggioni, Gustave Davis, Frederick Warner, Frank Geshwind, Andreas Coppi, Richard DeVerse, Ronald Coifman

Proceedings Volume 6091, 60910I (2006) https://doi.org/10.1117/12.646078

KEYWORDS: Biopsy, Tissues, Tissue optics, Light sources, Biological research, Colon, Data acquisition, Prototyping, Diagnostics, Signal to noise ratio

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We apply a unique micro-optoelectromechanical tuned light source and new algorithms to the hyper-spectral microscopic analysis of human colon biopsies. The tuned light prototype (Plain Sight Systems Inc.) transmits any combination of light frequencies, range 440nm 700nm, trans-illuminating H and E stained tissue sections of normal (N), benign adenoma (B) and malignant carcinoma (M) colon biopsies, through a Nikon Biophot microscope. Hyper-spectral photomicrographs, randomly collected 400X magnication, are obtained with a CCD camera (Sensovation) from 59 different patient biopsies (20 N, 19 B, 20 M) mounted as a microarray on a single glass slide. The spectra of each pixel are normalized and analyzed to discriminate among tissue features: gland nuclei, gland cytoplasm and lamina propria/lumens. Spectral features permit the automatic extraction of 3298 nuclei with classification as N, B or M. When nuclei are extracted from each of the 59 biopsies the average classification among N, B and M nuclei is 97.1%; classification of the biopsies, based on the average nuclei classification, is 100%. However, when the nuclei are extracted from a subset of biopsies, and the prediction is made on nuclei in the remaining biopsies, there is a marked decrement in performance to 60% across the 3 classes. Similarly the biopsy classification drops to 54%. In spite of these classification differences, which we believe are due to instrument and biopsy normalization issues, hyper-spectral analysis has the potential to achieve diagnostic efficiency needed for objective microscopic diagnosis.

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