Hybrid deep learning network for vascular segmentation in photoacoustic imaging

Photoacoustic (PA) technology has been used extensively on vessel imaging due to its capability of identifying molecular specificities and achieving high optical-diffraction-limited lateral resolution down to the cellular level. Vessel images carry essential medical information that provides guidelines for a professional diagnosis. Modern image processing techniques provide a decent contribution to vessel segmentation. However, these methods suffer from under or over-segmentation. Thus, we demonstrate both the results of adopting a fully convolutional network and U-net, and propose a hybrid network consisting of both applied on PA vessel images. Comparison results indicate that the hybrid network can significantly increase the segmentation accuracy and robustness.