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Proceedings Paper

Inferring nonlinear optical contrast from linear reflectance texture (Conference Presentation)

Paper Abstract

Nonlinear and ultrafast microscopy techniques enable label-free chemical imaging with high sensitivity, specificity, and optical resolution. However, reliance on specialized high-intensity femtosecond laser sources makes these techniques expensive and introduces a risk for sample damage. Simpler linear imaging methods, such as reflectance confocal microscopy, only sense variations in refractive index, and lack clear contrast provided by nonlinear techniques. But in the case of biological samples, where there is often a structure-function relationship (e.g. a cell’s mitochondrial network dynamically rearranges itself in response to metabolic activity), the kind of chemical information picked up by nonlinear techniques might be inferred from linear reflectance texture. If such a mapping can be learned, multiphoton-like contrast could be synthesized with images from much simpler instrumentation. Our approach to synthetic nonlinear microscopy employs machine learning technique involving convolutional neural network, namely U-net, that has demonstrated promising performance in segmentation of biomedical images. We have incorporated a nonlinear laser-scanning microscope with a confocal detection channel in order to acquire a training dataset of co-registered reflectance and nonlinear images. Results will be presented, along with a discussion on how well we can expect the trained network to generalize to new specimens.

Paper Details

Date Published: 9 September 2019
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Proc. SPIE 11122, Ultrafast Nonlinear Imaging and Spectroscopy VII, 111220P (9 September 2019);
Show Author Affiliations
Arya C. Mugdha, Colorado State Univ. (United States)
Jesse W. Wilson, Colorado State Univ. (United States)


Published in SPIE Proceedings Vol. 11122:
Ultrafast Nonlinear Imaging and Spectroscopy VII
Zhiwen Liu; Demetri Psaltis; Kebin Shi, Editor(s)

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