Efficient Segmentation of Multi-modal Optoacoustic and Ultrasound Images Using Convolutional Neural Networks

Abstract

Multispectral optoacoustic tomography (MSOT) offers the unique capability to map the distribution of spectrally distinctive endogenous and exogenous substances in heterogeneous biological tissues by exciting the sample at various wavelengths and detecting the optoacoustically-induced ultrasound waves. This powerful functional and molecular imaging capability can greatly benefit from hybridization with pulse-echo ultrasound (US), which provides additional information on tissue anatomy and blood flow. However, speed of sound variations and acoustic mismatches in the imaged object generally lead to errors in the coregistration of compounded images and loss of spatial resolution in both imaging modalities. The spatially- and wavelength-dependent light fluence attenuation further limits the quantitative capabilities of MSOT. Proper segmentation of different regions and assignment of corresponding acoustic and optical properties turns then essential for maximizing the performance of hybrid optoacoustic and ultrasound (OPUS) imaging. Particularly, accurate segmentation of the boundary of the sample can significantly improve the images rendered. Herein, we propose an automatic segmentation method based on a convolutional neural network (CNN) for segmenting the mouse boundary in a pre-clinical OPUS system. The experimental performance of the method, as characterized with the Dice coefficient metric between the network output and the ground truth (manually segmented) images, is shown to be superior than that of a state-of-the-art active contour segmentation method in a series of two-dimensional (cross-sectional) OPUS images of the mouse brain, liver and kidney regions.