The highly complementary information provided by multispectral optoacoustics and pulseecho ultrasound have recently prompted development of hybrid imaging instruments bringing together the unique contrast advantages of both modalities. In the hybrid optoacoustic ultrasound (OPUS) combination, images retrieved by one modality may further be used to improve the reconstruction accuracy of the other. In this regard, image segmentation plays a major role as it can aid improving the image quality and quantification abilities by facilitating modeling of light and sound propagation through the imaged tissues and surrounding coupling medium. Here, we propose an automated approach for surface segmentation in whole-body mouse OPUS imaging using a deep convolutional neural network (CNN). The method has shown robust performance, attaining accurate segmentation of the animal boundary in both optoacoustic and pulse-echo ultrasound images, as evinced by quantitative performance evaluation using Dice coefficient metrics.
Highlights
A multi-modal platform is introduced for simultaneous imaging of optical absorption, ultrasound reflectivity and speed of sound with improved resolution in whole living mice.
In vivo studies of 4T1 breast cancer xenografts models revealed highly synergistic value of the hybrid imaging for characterizing mammary tumors.
Multi-parametric characterization of tumor boundaries and neovascularization was demonstrated.
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