Automatic MRI-based Three-dimensional Models of Hip Cartilage Provide Improved Morphologic and Biochemical Analysis

• Published in: Clinical orthopaedics and related research 2019-05, Vol.477 (5), p.1036-1052
• Main Authors: Schmaranzer, Florian, Helfenstein, Ronja, Zeng, Guodong, Lerch, Till D, Novais, Eduardo N, Wylie, James D, Kim, Young-Jo, Siebenrock, Klaus A, Tannast, Moritz, Zheng, Guoyan
• Format: Article
• Language: English
• Subjects: Adult; Cartilage, Articular - diagnostic imaging; Female; Hip Joint - diagnostic imaging; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Male; Middle Aged; Models, Anatomic; Osteoarthritis, Hip - diagnostic imaging; Reproducibility of Results; Retrospective Studies; Young Adult
• ISSN: 0009-921X; 1528-1132; 0009-921X
• DOI: 10.1097/CORR.0000000000000755

The time-consuming and user-dependent postprocessing of biochemical cartilage MRI has limited the use of delayed gadolinium-enhanced MRI of cartilage (dGEMRIC). An automated analysis of biochemical three-dimensional (3-D) images could deliver a more time-efficient and objective evaluation of cartilage composition, and provide comprehensive information about cartilage thickness, surface area, and volume compared with manual two-dimensional (2-D) analysis. (1) How does the 3-D analysis of cartilage thickness and dGEMRIC index using both a manual and a new automated method compare with the manual 2-D analysis (gold standard)? (2) How does the manual 3-D analysis of regional patterns of dGEMRIC index, cartilage thickness, surface area and volume compare with a new automatic method? (3) What is the interobserver reliability and intraobserver reproducibility of software-assisted manual 3-D and automated 3-D analysis of dGEMRIC indices, thickness, surface, and volume for two readers on two time points? In this IRB-approved, retrospective, diagnostic study, we identified the first 25 symptomatic hips (23 patients) who underwent a contrast-enhanced MRI at 3T including a 3-D dGEMRIC sequence for intraarticular pathology assessment due to structural hip deformities. Of the 23 patients, 10 (43%) were male, 16 (64%) hips had a cam deformity and 16 (64%) hips had either a pincer deformity or acetabular dysplasia. The development of an automated deep-learning-based approach for 3-D segmentation of hip cartilage models was based on two steps: First, one reader (FS) provided a manual 3-D segmentation of hip cartilage, which served as training data for the neural network and was used as input data for the manual 3-D analysis. Next, we developed the deep convolutional neural network to obtain an automated 3-D cartilage segmentation that we used as input data for the automated 3-D analysis. For actual analysis of the manually and automatically generated 3-D cartilage models, a dedicated software was developed. Manual 2-D analysis of dGEMRIC indices and cartilage thickness was performed at each "full-hour" position on radial images and served as the gold standard for comparison with the corresponding measurements of the manual and the automated 3-D analysis. We measured dGEMRIC index, cartilage thickness, surface area, and volume for each of the four joint quadrants and compared the manual and the automated 3-D analyses using mean differences. Agreement between the techniques