基于磁共振成像（MRI）的髋关节三维模型，能够在无辐射条件下，利用深度学习进行自动分割，对髋臼周围截骨术治疗髋关节发育不良实施计算机辅助规划。

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.

作者信息

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

机构信息

Sitem Center for Translational Medicine and Biomedical Entrepreneurship, University of Bern, Switzerland.

Department of Orthopedic Surgery, Inselspital, University of Bern, Bern, Switzerland.

出版信息

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

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 mm 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.

引言

髋关节发育不良（DDH）和股骨髋臼撞击症（FAI）都是复杂的三维髋关节病变，可导致年轻患者髋关节疼痛和骨关节炎。基于三维磁共振成像（3D-MRI）的模型被用于无辐射的计算机辅助手术规划。基于MRI的三维模型的自动分割更受青睐，因为手动分割耗时。本研究旨在探讨（1）基于磁共振成像自动分割的三维模型与基于计算机断层扫描（CT）手动分割的三维模型在股骨头覆盖率（FHC）方面的差异及（2）相关性，以及（3）对有症状的髋关节疾病患者进行术前规划的可行性。

方法

我们对31例髋关节（26例有症状的髋关节发育不良或股骨髋臼撞击症患者）进行了一项经机构审查委员会（IRB）批准的对比性回顾性研究。获取了髋关节的3D MRI序列和CT扫描图像。术前MRI包括髋关节的轴向斜位T1加权容积内插屏气检查（VIBE）序列（等体素0.8毫米）。对MRI和CT扫描图像进行了手动分割。使用深度学习对基于MRI的三维模型进行了自动分割。

结果

（1）基于MRI的三维髋关节模型自动分割与手动分割之间的差异小于1毫米（股骨近端0.2±0.1毫米，髋臼0.3±0.5毫米）。股骨近端和髋臼的骰子系数分别为98%和97%。（2）基于MRI自动分割的三维模型与基于CT手动分割的三维模型在总FHC方面的相关性极佳且具有显著性（r = 0.975，p < 0.001）。基于MRI自动分割的三维模型与手动分割的三维模型在总FHC方面的相关性也极佳（r = 0.979，p < 0.001）。（3）对所有髋关节发育不良或髋臼后倾患者（100%）进行髋臼周围截骨术的术前规划和模拟是可行的。