Magnetic Resonance Imaging-Based 3-Dimensional Models of the Pelvis and Hip Using Machine Learning for Automatic Bone Segmentation in a Dynamic Hip Impingement Simulation.

Background: Femoroacetabular impingement (FAI) can cause hip pain and osteoarthritis in patients. Importantly, 3-dimensional (3D) bone models in dynamic hip impingement simulations can enable a patient-specific diagnosis of FAI. The manual segmentation of bone models is time-consuming; therefore, automatic segmentation should be investigated. Purpose: To investigate the difference between manual and automatic segmentation of magnetic resonance imaging (MRI)-based 3D bone models of the hip, (2) to correlate impingement-free hip range of motion, and (3) to determine external validation. Study Design: Cohort study (Diagnosis); Level of evidence, 3. Methods: An institutional review board-approved retrospective study involving a total of 98 hips was performed. Of these, 30 patients with symptomatic FAI (60 hips; mean age, 27 ± 9 years) and 19 asymptomatic participants (38 hips) underwent 3-T MRI of the hip including a rapid 3D T1-weighted VIBE Dixon sequence of the pelvis (192 images acquired in 32 seconds). The automatic segmentation of MRI-based 3D bone models was performed using machine learning (convolutional neural network). The Dice similarity coefficient (DSC) was calculated for 98 hips to assess the overlap and difference in MRI-based 3D bone models with 5-fold cross-validation. Automatic segmentation was assessed with 16 patients (32 hips) from another institution for external validation. Impingement-free range of motion was compared between the manual and automatic segmentation of MRI-based 3D bone models (30 patients with FAI). Results: The difference between the manual and automatic segmentation of MRI-based 3D bone models was <1 mm (<0.6 mm for pelvic models and <0.5 mm for femoral models), and the DSC of the 30 patients with FAI was 94.1% (pelvis) and 97.0% (proximal femur); for the 19 asymptomatic participants, the difference was <0.5 mm, and the DSC was 95.5% and 97.5%, respectively. The correlation for impingement-free flexion ( Conclusion: The automatic segmentation of MRI-based 3D bone models was as accurate as the manual segmentation of MRI-based 3D bone models for patients with FAI. It allows radiation-free and patient-specific preoperative surgical planning of hip preservation surgery and hip arthroscopic surgery for patients with FAI of a childbearing age. The automatic segmentation of MRI-based 3D bone models using deep learning was feasible with routine MRI (3D T1-weighted VIBE Dixon sequence) with a short image acquisition time (<1 minute).