Cellular refractive index and thickness recovery via unsupervised learning framework.

Arturo Burguete-Lopez; Maksim O. Makarenko; Fedor Getman; Andrea Fratalocchi

doi:10.1117/12.2608574

3 March 2022 Cellular refractive index and thickness recovery via unsupervised learning framework.

Arturo Burguete-Lopez, Maksim O. Makarenko, Fedor Getman, Andrea Fratalocchi

Proceedings Volume PC11976, Nanoscale Imaging, Sensing, and Actuation for Biomedical Applications XIX; PC1197602 (2022) https://doi.org/10.1117/12.2608574
Event: SPIE BiOS, 2022, San Francisco, California, United States

Abstract

This work presents an AI-driven framework to extract the biological tissue's refractive index and thickness maps from a single RGB image. This approach is based on a physical light-trapping surface and an unsupervised inverse search projector which projects given RGB pixel to the sample's refractive index and thickness at the corresponding coordinate.

Conference Presentation

Citation Download Citation

Arturo Burguete-Lopez, Maksim O. Makarenko, Fedor Getman, and Andrea Fratalocchi "Cellular refractive index and thickness recovery via unsupervised learning framework.", Proc. SPIE PC11976, Nanoscale Imaging, Sensing, and Actuation for Biomedical Applications XIX, PC1197602 (3 March 2022); https://doi.org/10.1117/12.2608574

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