Long-short-term-memory cells enable flexible deep learning-based photoacoustic oximetry

Kevin Gu; Kylie Yeung; Michael Doherty; Lina Hacker; Thomas R. Else; Sarah E. Bohndiek; Janek Gröhl

doi:10.1117/12.2650038

9 March 2023 Long-short-term-memory cells enable flexible deep learning-based photoacoustic oximetry

Kevin Gu, Kylie Yeung, Michael Doherty, Lina Hacker, Thomas R. Else, Sarah E. Bohndiek, Janek Gröhl

Proceedings Volume PC12379, Photons Plus Ultrasound: Imaging and Sensing 2023; PC123791H (2023) https://doi.org/10.1117/12.2650038
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

Machine learning-based approaches have shown promise for quantitative photoacoustic oximetry, however, the impact of learned methods is hampered by challenges of usability and generalisability, caused by the strong dependence of learned methods on the training data sets. To address these issues we developed a deep learning-based approach with higher flexibility. The method is trained on a suite of training data sets representing a range of general assumptions. The performance is systematically compared to linear unmixing methods and is validated on in silico, in vitro, and in vivo data representing different use cases.

Conference Presentation

Citation Download Citation

Kevin Gu, Kylie Yeung, Michael Doherty, Lina Hacker, Thomas R. Else, Sarah E. Bohndiek, and Janek Gröhl "Long-short-term-memory cells enable flexible deep learning-based photoacoustic oximetry", Proc. SPIE PC12379, Photons Plus Ultrasound: Imaging and Sensing 2023, PC123791H (9 March 2023); https://doi.org/10.1117/12.2650038

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