Presentation
9 September 2019 Inferring nonlinear optical contrast from linear reflectance texture (Conference Presentation)
Author Affiliations +
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.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Arya C. Mugdha and Jesse W. Wilson "Inferring nonlinear optical contrast from linear reflectance texture (Conference Presentation)", Proc. SPIE 11122, Ultrafast Nonlinear Imaging and Spectroscopy VII, 111220P (9 September 2019); https://doi.org/10.1117/12.2527357
Advertisement
Advertisement
KEYWORDS
Reflectivity

Confocal microscopy

Image segmentation

Microscopy

Femtosecond phenomena

Imaging spectroscopy

Nonlinear optics

Back to Top