Related Experiment Video
Updated: Jan 17, 2026
In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy
Published on: July 2, 2021
Magnetic Resonance Imaging-Based 3-Dimensional Models of the Pelvis and Hip Using Machine Learning for Automatic Bone
Till D Lerch1, Guodong Zeng2, Adam Boschung3
1Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, University of Bern, Bern, Switzerland.
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 (r = 0.93; P < .001) was excellent (30 patients with FAI). The mean difference in flexion and internal rotation at 90° of flexion was 2.9°± 4.0° and 3.0°± 4.0°, respectively. The DSC of the 16 patients from another institution was 92.2% (pelvis) and 94.9% (proximal femur) for external validation.
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).
