Echocardiogram localization using barycentric interpolation

Medical ultrasound is a ubiquitous, non-invasive, and relatively inexpensive technology used for a wide array of diagnostic tasks. In the case of 2D handheld ultrasound, the positioning of the probe has a direct impact on the diagnostic relevance of the acquired images; shifts of as little as a few millimeters can render the images unusable. We present a method which interpolates the predictions of a deep convolutional neural network classifier to estimate the viewpoint of the imaging probe directly from the visual data. For the discrete version of echocardiogram view classification, our method outperforms recent approaches on real-world data.