Generation of training data for quantitative photoacoustic imaging

Melanie Schellenberg; Janek Gröhl; Kris Dreher; Niklas Holzwarth; Minu D. Tizabi; Alexander Seitel; Lena Maier-Hein

doi:10.1117/12.2578180

5 March 2021 Generation of training data for quantitative photoacoustic imaging

Melanie Schellenberg, Janek Gröhl, Kris Dreher, Niklas Holzwarth, Minu D. Tizabi, Alexander Seitel, Lena Maier-Hein

Proceedings Volume 11642, Photons Plus Ultrasound: Imaging and Sensing 2021; 116421J (2021) https://doi.org/10.1117/12.2578180
Event: SPIE BiOS, 2021, Online Only

Abstract

Photoacoustic imaging (PAI) is an emerging medical imaging modality that provides high contrast and spatial resolution. A core unsolved problem to effectively support interventional healthcare is the accurate quantification of the optical tissue properties, such as the absorption and scattering coefficients. The contribution of this work is two-fold. We demonstrate the strong dependence of deep learning-based approaches on the chosen training data and we present a novel approach to generating simulated training data. According to initial in silico results, our method could serve as an important first step related to generating adequate training data for PAI applications.

Conference Presentation

Citation Download Citation

Melanie Schellenberg, Janek Gröhl, Kris Dreher, Niklas Holzwarth, Minu D. Tizabi, Alexander Seitel, and Lena Maier-Hein "Generation of training data for quantitative photoacoustic imaging", Proc. SPIE 11642, Photons Plus Ultrasound: Imaging and Sensing 2021, 116421J (5 March 2021); https://doi.org/10.1117/12.2578180

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