A registration framework based on the bone surface extraction from 3D freehand US and a subsequent fast, automatic surface alignment robust to single-sided view and large false-positive rates from US was shown to achieve registration accuracy feasible for practical orthopedic scenarios and a qualitative outcome indicating good visual image alignment.

For computer-assisted interventions in orthopedic surgery, automatic bone surface delineation can be of great value. For instance, given such a method, an automatically extracted bone surface from intraoperative imaging modalities can be registered to the bone surfaces from preoperative images, allowing for enhanced visualization and/or surgical guidance. Ultrasound (US) is ideal for imaging bone surfaces intraoperatively, being real-time, non-ionizing, and cost-effective. However, due to its low signal-to-noise ratio and imaging artifacts, extracting bone surfaces automatically from such images remains challenging. In this work, we examine the suitability of deep learning for automatic bone surface extraction from US. Given 1800 manually annotated US frames, we examine the performance of two popular neural networks used for segmentation. Furthermore, we investigate the effect of different preprocessing methods used for manual annotations in training on the final segmentation quality, and demonstrate excellent qualitative and quantitative segmentation results.