Precise Ultrasound Bone Registration with Learning-Based Segmentation and Speed of Sound Calibration

Mehrdad Salehi, Raphael Prevost, José Luis Moctezuma, Nassir Navab, Wolfgang Wein

Abstract: Ultrasound imaging is increasingly used in navigated surgery and registration-based applications. However, spatial information quality in ultrasound is relatively inferior to other modalities. Main limiting factors for an accurate registration between ultrasound and other modalities are tissue deformation and speed of sound variation throughout the body. The bone surface in ultrasound is a landmark which is less affected by such geometric distortions. In this paper, we present a workflow to accurately register intra-operative ultrasound images to a reference pre-operative CT volume based on an automatic and real-time image processing pipeline. We show that a convolutional neural network is able to produce robust, accurate and fast bone segmentation of such ultrasound images. We also develop a dedicated method to perform online speed of sound calibration by focusing on the bone area and optimizing the appearance of steered compounded images. We provide extensive validation on both phantom and real cadaver data obtaining overall errors under one millimeter.