Automated cellular pathology in noninvasive confocal microscopy

Monica Ting; James Krueger; Daniel Gareau

doi:10.1117/12.2040213

17 March 2014 Automated cellular pathology in noninvasive confocal microscopy

Monica Ting, James Krueger, Daniel Gareau

Author Affiliations +

Proceedings Volume 8940, Optical Biopsy XII; 89400A (2014) https://doi.org/10.1117/12.2040213
Event: SPIE BiOS, 2014, San Francisco, California, United States

Abstract

A computer algorithm was developed to automatically identify and count melanocytes and keratinocytes in 3D reflectance confocal microscopy (RCM) images of the skin. Computerized pathology increases our understanding and enables prevention of superficial spreading melanoma (SSM). Machine learning involved looking at the images to measure the size of cells through a 2-D Fourier transform and developing an appropriate mask with the erf() function to model the cells. Implementation involved processing the images to identify cells whose image segments provided the least difference when subtracted from the mask. With further simplification of the algorithm, the program may be directly implemented on the RCM images to indicate the presence of keratinocytes in seconds and to quantify the keratinocytes size in the en face plane as a function of depth. Using this system, the algorithm can identify any irregularities in maturation and differentiation of keratinocytes, thereby signaling the possible presence of cancer.

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Monica Ting, James Krueger, and Daniel Gareau "Automated cellular pathology in noninvasive confocal microscopy", Proc. SPIE 8940, Optical Biopsy XII, 89400A (17 March 2014); https://doi.org/10.1117/12.2040213

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KEYWORDS

Image segmentation

Confocal microscopy

Melanoma

Skin

Spatial frequencies

Algorithm development

Fourier transforms

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