Ultrasound-based Navigation of Scaphoid Fracture Surgery

For minimally-invasive surgery of the scaphoid, navigation based on ultrasound images instead of fluoroscopy reduces costs and prevents 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 in-vitro 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.