Target Tracking in 4D Ultrasound using Localization Networks

Abstract

In radiation therapy, breathing and other influences cause a constant movement of the tissue to be irradiated. Thus, a continuous position control is required which could be handled by the usage of 3D ultrasound imaging. For this purpose, two approaches for target tracking in 3D ultrasound (US) sequences of the liver are analyzed in this study. Therefore, an image-by-image localization of the target is performed using a deep localization network. A singletarget and a multiple-target approach are investigated where deep localization networks are trained for locating one specific and multiple specific targets, respectively. Training the networks and evaluating the tracking algorithm is performed on the basis of a labeled 3D US liver data set in 2-fold crossvalidation experiments. The single-target and multiple-target approaches performed comparable with a mean tracking error of 2.28±1.20mm and 2.23±1.28 mm, respectively. The proposed tracking algorithm is real-time capable with a mean runtime per 3D ultrasound image of 68ms.

Keywords

• convolutional neural network
• sonography