Semantic segmentation of multispectral photoacoustic images using deep learning

TL;DR

This paper introduces a deep learning method for semantic segmentation of multispectral photoacoustic images, enhancing interpretability and clinical translation by automating tissue identification in high-dimensional data.

Contribution

It presents a supervised deep learning approach for tissue segmentation in multispectral photoacoustic images, validated on human data, aiding clinical application.

Findings

Automatic tissue segmentation improves image interpretability.

Deep learning method successfully applied to human volunteer data.

Segmentation facilitates analysis and visualization of multispectral PA images.

Abstract

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.