Ultrasound-based Navigation of Scaphoid Fracture Surgery
Peter Broessner, Benjamin Hohlmann, Klaus Radermacher
Rheinisch-Westfälische Technische Hochschule Aachen, Lehrstuhl für Medizintechnik
Abstract
For minimally-invasive surgery of the scaphoid, navigation based on ultrasound images instead of uoroscopy could reduce costs as well as prevent exposure to ionizing radiation. We present a machine learning based two-stage approach that tackles the tasks of image segmentation and point cloud registration individually. For this, Deeplabv3+ as well as the PRNet architecture were trained on two newly generated datasets. An evaluation on invitro data results in an average surface distance error of 1.1mm and a mean rotational deviation of 6:2° with a processing time of 9 seconds. We conclude that near real-time navigation is feasible.