Zeng, Guodong; Schmaranzer, Florian; Degonda, Celia; Gerber, Nicolas; Gerber, Kate; Tannast, Moritz; Burger, Jürgen; Siebenrock, Klaus A.; Zheng, Guoyan; Lerch, Till (2021). MRI-based 3D models of the hip joint enables radiation-free computer-assisted planning of periacetabular osteotomy for treatment of hip dysplasia using deep learning for automatic segmentation. European journal of radiology open, 8, p. 100303. Elsevier 10.1016/j.ejro.2020.100303
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Introduction: Both Hip Dysplasia(DDH) and Femoro-acetabular-Impingement(FAI) are complex three-dimensional hip pathologies causing hip pain and osteoarthritis in young patients. 3D-MRI-based models were used for radiation-free computer-assisted surgical planning. Automatic segmentation of MRI-based 3D-models are preferred because manual segmentation is time-consuming.
To investigate(1) the difference and(2) the correlation for femoral head coverage(FHC) between automatic MR- based and manual CT-based 3D-models and (3) feasibility of preoperative planning in symptomatic patients with hip diseases.
Methods: We performed an IRB-approved comparative, retrospective study of 31 hips(26 symptomatic patients with hip dysplasia or FAI). 3D MRI sequences and CT scans of the hip were acquired. Preoperative MRI included axial-oblique T1 VIBE sequence(0.8 mm3 isovoxel) of the hip joint. Manual segmentation of MRI and CT scans were performed. Automatic segmentation of MRI-based 3D-models was performed using deep learning. Results: (1)The difference between automatic and manual segmentation of MRI-based 3D hip joint models was below 1mm(proximal femur 0.2±0.1mm and acetabulum 0.3±0.5mm). Dice coefficients of the proximal femur and the acetabulum were 98 % and 97 %, respectively. (2)The correlation for total FHC was excellent and significant(r = 0.975, p < 0.001) between automatic MRI-based and manual CT-based 3D-models. Correlation for total FHC (r = 0.979, p < 0.001) between automatic and manual MR-based 3D models was excellent. (3)Preoperative planning and simulation of periacetabular osteotomy was feasible in all patients(100 %) with hip dysplasia or acetabular retroversion.
Conclusions: Automatic segmentation of MRI-based 3D-models using deep learning is as accurate as CT-based 3D- models for patients with hip diseases of childbearing age. This allows radiation-free and patient-specific pre- operative simulation and surgical planning of periacetabular osteotomy for patients with DDH.
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