Ultrasound Bone Surface Segmentation for Hip Joint Arthroscopy: Evaluating a Local Phase-Based and a Rigid Object Filtering in a Simulated Environment

Arthroscopy is a well-known procedure, classified as a minimally invasive procedure, the objective is to image inside the joints, as the hip joint. Although its use is being prioritized over others, this procedure is not exempt from certain complications, such as disorientation, reduced area of vision and loss of depth perception. To address this problem, clinicians relays on imaging systems for guidance, as Ultrasound (US). However, US presents some challenges, including a low signal-to-noise ratio, the need to address speckle noise, and a considerable learning curve. Efforts have been made to improve US-based bone detection so that it can be integrated into computer-assisted orthopedic surgery (CAOS) systems. In this paper, a bone surface segmentation algorithm based on local phases and combined with a rigid object filtering is presented. This algorithm is implemented in a simulated environment, where a 3D print of the hip joint is used as a target and a low-cost mannequin is made for soft tissue simulation. The evaluation metrics are presented, being a F-Score (0.979), Accuracy (0.9796), Recall (0.9883), and Hamming Loss (0.024).