Generative adversarial network prediction of optical properties from wide-field images (Conference Presentation)

Nicholas J. Durr

doi:10.1117/12.2550569

10 March 2020 Generative adversarial network prediction of optical properties from wide-field images (Conference Presentation)

Nicholas J. Durr

Author Affiliations +

Proceedings Volume 11222, Molecular-Guided Surgery: Molecules, Devices, and Applications VI; 1122203 (2020) https://doi.org/10.1117/12.2550569
Event: SPIE BiOS, 2020, San Francisco, California, United States

Abstract

I will present a deep learning framework for content-aware estimation of tissue optical properties from wide-field images. Spatial frequency domain imaging is used to acquire ground-truth measurements of scattering and absorption coefficients of a variety of tissues. A generative network is then adversarially trained to estimate these properties from new tissues directly from unstructured or structured light. This data-driven approach has some advantages in accuracy and speed compared to model-based approaches.

Conference Presentation

Citation Download Citation

Nicholas J. Durr "Generative adversarial network prediction of optical properties from wide-field images (Conference Presentation)", Proc. SPIE 11222, Molecular-Guided Surgery: Molecules, Devices, and Applications VI, 1122203 (10 March 2020); https://doi.org/10.1117/12.2550569

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available