Semantic segmentation of multispectral photoacoustic images using deep learning

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

doi:10.1117/12.2578135

5 March 2021 Semantic segmentation of multispectral photoacoustic images using deep learning

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

Proceedings Volume 11642, Photons Plus Ultrasound: Imaging and Sensing 2021; 116423F (2021) https://doi.org/10.1117/12.2578135
Event: SPIE BiOS, 2021, Online Only

Conference Poster

Abstract

Photoacoustic imaging (PAI) has the potential to revolutionize healthcare due to the valuable information on tissue physiology that is contained in multispectral signals. Clinical translation of the technology requires conversion of the high-dimensional acquired data into clinically relevant and interpretable information. In this work, we present a deep learning-based approach to semantic segmentation of multispectral PA images to facilitate interpretability of recorded images. Based on a validation study with experimentally acquired data of healthy human volunteers, we show that a combination of tissue segmentation, sO2 estimation, and uncertainty quantification can create powerful analyses and visualizations of multispectral photoacoustic images.

Conference Presentation

Citation Download Citation

Janek Gröhl, Melanie Schellenberg, Kris K. Dreher, Niklas Holzwarth, Minu D. Tizabi, Alexander Seitel, and Lena Maier-Hein "Semantic segmentation of multispectral photoacoustic images using deep learning", Proc. SPIE 11642, Photons Plus Ultrasound: Imaging and Sensing 2021, 116423F (5 March 2021); https://doi.org/10.1117/12.2578135

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