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

Guodong Zeng; Florian Schmaranzer; Celia Degonda; Nicolas Gerber; Kate Gerber; Moritz Tannast; Jürgen Burger; Klaus A Siebenrock; Guoyan Zheng; Till D Lerch

doi:10.1016/j.ejro.2020.100303

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

Eur J Radiol Open. 2020 Dec 18:8:100303. doi: 10.1016/j.ejro.2020.100303. eCollection 2021.

Authors

Guodong Zeng¹, Florian Schmaranzer^{2

3}, Celia Degonda², Nicolas Gerber¹, Kate Gerber¹, Moritz Tannast^{2

4}, Jürgen Burger¹, Klaus A Siebenrock², Guoyan Zheng⁵, Till D Lerch^{2

3}

Affiliations

¹ Sitem Center for Translational Medicine and Biomedical Entrepreneurship, University of Bern, Switzerland.
² Department of Orthopedic Surgery, Inselspital, University of Bern, Bern, Switzerland.
³ Department of Diagnostic, Interventional and Paediatric Radiology, University of Bern, Inselspital, Bern, Switzerland.
⁴ Department of Orthopaedic Surgery and Traumatology, Cantonal Hospital, University of Fribourg, Switzerland.
⁵ Institute for Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, China.

Abstract

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 1 mm(proximal femur 0.2 ± 0.1 mm and acetabulum 0.3 ± 0.5 mm). 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 preoperative simulation and surgical planning of periacetabular osteotomy for patients with DDH.

Keywords: ASD, Average Surface Distance; Automatic segmentation; DDH; DDH, developmental dysplasia of the hip; DOC, Dice Overlap Coefficients; Deep learning; FAI, Femoroacetabular Impingement; FHC, femoral head center; GPU, Graphics processing unit; Hip dysplasia; Hip joint; MRI; Machine learning; Magnetic resonance imaging; ROM, range of motion.