Automatic reconstruction and separation of each constituent’s absorption and scattering properties using a customized autoencoder neural network

Dongqin Ni; Niklas Karmann; Martin Hohmann

doi:10.1117/12.3021547

18 June 2024 Automatic reconstruction and separation of each constituent’s absorption and scattering properties using a customized autoencoder neural network

Dongqin Ni, Niklas Karmann, Martin Hohmann

Author Affiliations +

Proceedings Volume 13010, Tissue Optics and Photonics III; 130100H (2024) https://doi.org/10.1117/12.3021547
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

Investigating optical properties (OPs) is crucial in the field of biophotonics. Various techniques are available for deriving OPs, with inverse Monte Carlo simulations (IMCS) being the most advanced for ex-vivo contexts. However, identifying the spectral behavior of each microscopic absorber and scatterer responsible for generating these OPs requires further experimentation. To tackle this issue, a customized autoencoder neural network (ANN) is suggested. The ANN computes OPs from measurements, where the bottleneck corresponds to the number of absorbers and scatterers. The presented ANN functions asymmetrically and computes the final OPs using a linear combination of absorbers and scatterers. Consequently, the decoder’s weight corresponds to the constituent’s OPs spectral behavior. Validation was conducted by utilizing intralipid as a scatterer and ink as an absorber. The employment of the decoder weights facilitated the successful extraction of the spectral shape of every constituent.

Conference Presentation

Citation Download Citation

Dongqin Ni, Niklas Karmann, and Martin Hohmann "Automatic reconstruction and separation of each constituent’s absorption and scattering properties using a customized autoencoder neural network", Proc. SPIE 13010, Tissue Optics and Photonics III, 130100H (18 June 2024); https://doi.org/10.1117/12.3021547

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