3D Freehand Ultrasound Calibration with Convolutional Networks

Project type: Master thesis
Advisor: Nassir Navab
Supervision by: Rüdiger Göbl

Thesis by: Arianne Tran

Abstract

Ultrasound calibration, a means of determining the spatial relationship between the (2D) image coordinate system of the B-scan and a position sensor attached to the transducer, is a vital step for several applications. These range from US guided interventions to 3D volume reconstruction. However, most calibration techniques either require expertise in terms of ultrasound image acquisition, their accuracy is limited by physical phenomena or are tedious to perform. We propose a technique to learn the calibration, from an easily reproducible high accuracy phantom, by means of deep learning techniques. The easy availability of Lego and minimal deviations from standardized sizes, make this an attractive component for reproducibility. The aim of this work is to achieve an easily applicable calibration technique, which will be compared against other common calibration techniques in terms of accuracy.