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Proceedings Paper

Machine learning approach to synthesizing multiphoton microscopic images from reflectance confocal (Conference Presentation)

Paper Abstract

Multiphoton microscopy imaging techniques provide molecule specific contrast and can produce in vivo histopathology with clearly recognizable features such as cellular and nuclear morphology, collagen, etc. Despite this advantage, high cost, risk of damage from high-intensity pulses, and lack of FDA approval prevents widespread adoption of multiphoton microscopy techniques in conventional clinical scenario. Reflectance confocal microscopy, on the other hand, is much more affordable for clinical scenario, as it is FDA approved, can perform in vivo, non-invasive imaging of specimens with less risk of DNA damage, and even has been granted insurance reimbursement codes. However, the images obtained by reflectance confocal have little resemblance to traditional histopathology due to graininess in the images, and lack molecule specific contrast which makes the images more challenging to interpret and determine a diagnosis,. We propose brining multiphoton-like contrast to confocal instruments by a neural network trained on a set of co-registered reflectance confocal and multiphoton images. We assume that the local reflectance texture of cytoplasm, nuclei, melanin and cytoplasm are distinct within a cell. Once the neural network has been trained, it would be able to distinguish these structures, and produce clear histology-like images from the grainy confocal reflectance data. Our preliminary training results show a successful estimation of multiphoton images from reflectance confocal images by training a 3 layer neural network on a set of 1000 32x32 image patches.

Paper Details

Date Published: 4 March 2019
Proc. SPIE 10851, Photonics in Dermatology and Plastic Surgery 2019, 1085106 (4 March 2019); doi: 10.1117/12.2510473
Show Author Affiliations
Arya Chowdhury Mugdha, Colorado State Univ. (United States)
Jesse W. Wilson, Colorado State Univ. (United States)

Published in SPIE Proceedings Vol. 10851:
Photonics in Dermatology and Plastic Surgery 2019
Bernard Choi; Haishan Zeng, Editor(s)

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