Deep Learning‐Based Optical‐Resolution Photoacoustic Microscopy for In Vivo 3D Microvasculature Imaging and Segmentation

Abstract

Optical‐resolution photoacoustic microscopy (OR‐PAM) technique is a noninvasive imaging technique that can be used to obtain high‐resolution images. This technique does not require the use of exogenous contrast agents. The technique can be effectively used to realize endogenous and exogenous imaging of microvasculature. However, the depth of focus (DOF) realized using OR‐PAM is low and inadequate to cover the entire 3D data for microvasculature imaging. A varying extent of signal‐to‐noise ratio (SNR) is recorded when varying depths of the 3D sample are studied. This significantly hindered the processes of image recognition, segmentation, and analysis. Herein, a deep learning‐based OR‐PAM technique to image and analyze 3D datasets is proposed. Endogenous and exogenous multi‐organ imaging data are sequentially imaged and segmented, and excellent generalization ability is observed. Wide‐field and ultradense exogenous 3D imaging of mouse brain vasculature located at different depths is obtained and segmented by OR‐PAM for the first time. The results reveal that the proposed method can be used for effectively imaging the entire microvasculature. The method can be potentially used for imaging the microcirculation system in clinics.

Keywords

• deep learning
• endogenous and exogenous imaging
• microvasculature imaging
• optical-resolution photoacoustic microscopy