  Photoacoustic (PA) imaging has the potential to revolutionize functional
medical imaging in healthcare due to the valuable information on tissue
physiology contained in multispectral photoacoustic measurements. 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 photoacoustic images to facilitate image interpretability.
Manually annotated photoacoustic {and ultrasound} imaging data are used as
reference and enable the training of a deep learning-based segmentation
algorithm in a supervised manner. Based on a validation study with
experimentally acquired data from 16 healthy human volunteers, we show that
automatic tissue segmentation can be used to create powerful analyses and
visualizations of multispectral photoacoustic images. Due to the intuitive
representation of high-dimensional information, such a preprocessing algorithm
could be a valuable means to facilitate the clinical translation of
photoacoustic imaging.
