Automatic Segmentation for Analysis of Murine Cardiac Ultrasound and Photoacoustic Image Data Using Deep Learning.

Objective: Although there are methods to identify regions of interest (ROIs) from echocardiographic images of myocardial tissue, they are often time-consuming and difficult to create when image quality is poor. Further, while myocardial strain from ultrasound (US) images can be estimated, US alone cannot obtain functional information, such as oxygen saturation (sO). Photoacoustic (PA) imaging, however, can be used to quantify sO levels within tissue non-invasively.