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Femoral neck fractures have a high incidence in the geriatric population and are associated with high mortality and disability rates. With the minimally invasive nature, internal fixation is widely used as a treatment option to stabilize femoral neck fractures. The fixation effectiveness and stability of the implant is an essential guide for the surgeon. However, there is no long-term reliable evaluation method to quantify the implant’s fixation effect without affecting the patient’s behavior and synthesizing long-term treatment data. For the femur’s symmetrical structure, this study used 3D convolutional networks for biomedical image segmentation (3D-UNet) to segment the injured femur as a mask, aligned computerized tomography (CT) scans of the patient at different times after surgery and quantified the displacement in the specified direction using the generated 3D point cloud. In the experimental part, we used 10 groups containing two CT images scanned at the one-year interval after surgery. By comparing manual segmentation of femur and segmentation of femur as a mask using neural network, the mask obtained by segmentation using the 3D-UNet network with symmetric structure fully meets the requirements of image registration. The data obtained from the 3D point cloud calculation is within the error tolerance, and the calculated displacement of the implant can be visualized in 3D space.

symmetry

A Postoperative Displacement Measurement Method for Femoral Neck Fracture Internal Fixation Implants Based on Femoral Segmentation and Multi-Resolution Frame Registration