Semantic segmentation of multispectral photoacoustic images using deep learning
This work addresses the clinical translation challenge of photoacoustic imaging for healthcare by improving image interpretability, though it appears incremental as it applies existing deep learning methods to a new domain.
The paper tackles the problem of converting high-dimensional multispectral photoacoustic imaging data into clinically interpretable information by developing a deep learning-based semantic segmentation method, showing that automatic tissue segmentation can create powerful analyses and visualizations based on data from 16 healthy human volunteers.
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.