Abstract

Computer-assisted orthopedic surgery requires precise representations of bone surfaces. To date, computed tomography constitutes the gold standard, but comes with a number of limitations, including costs, radiation and availability. Ultrasound has potential to become an alternative to computed tomography, yet suffers from low image quality and limited field-of-view. These shortcomings may be addressed by a fully automatic segmentation and model-based completion of 3D bone surfaces from ultrasound images. This survey summarizes the state-of-the-art in this field by introducing employed algorithms, and determining challenges and trends. For segmentation, a clear trend toward machine learning-based algorithms can be observed. For 3D bone model completion however, none of the published methods involve machine learning. Furthermore, data sets and metrics are identified as weak spots in current research, preventing development and evaluation of models that generalize well.

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