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

Ni D, Karmann N, Hohmann M (2024)

Publication Type: Conference contribution

Publication year: 2024

Publisher: SPIE

Series: Proceedings of SPIE

Book Volume: 13010

Pages Range: 130100H

Conference Proceedings Title: Tissue Optics and Photonics III

Event location: Strasbourg FR

ISBN: 9781510673380

DOI: 10.1117/12.3021547

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.

Authors with CRIS profile

Dongqin Ni Lehrstuhl für Photonische Technologien (LPT) Martin Hohmann Lehrstuhl für Photonische Technologien (LPT)

How to cite

APA:

Ni, D., Karmann, N., & Hohmann, M. (2024). Automatic reconstruction and separation of each constituent's absorption and scattering properties using a customized autoencoder neural network. In Valery V. Tuchin, Walter C. Blondel, Zeev Zalevsky (Eds.), Tissue Optics and Photonics III (pp. 130100H). Strasbourg, FR: SPIE.

MLA:

Ni, Dongqin, Niklas Karmann, and Martin Hohmann. "Automatic reconstruction and separation of each constituent's absorption and scattering properties using a customized autoencoder neural network." Proceedings of the SPIE Photonics Europe, Strasbourg Ed. Valery V. Tuchin, Walter C. Blondel, Zeev Zalevsky, SPIE, 2024. 130100H.

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