Schematic of workflow to generate cartilage thickness field and bone shape model and classification of sex differences.
Schematic of workflow to generate cartilage thickness field and bone shape model and classification of sex differences. MR images (n = 51) of the knee were segmented, processed in MAPClient, and resampled (A) to produce bone and cartilage point clouds. Parametric or correspondent bone point clouds were obtained via an iterative fitting process (B). Subchondral bone node numbers on these correspondent point clouds were found by combining node numbers obtained from all subjects using a closest-point algorithm (C). Cartilage thickness maps were calculated (D) by computing the magnitude of the projection of the closest articular cartilage point to the normal vector of each subchondral bone node. Principal component analysis (E) was performed on features consisting of the corresponding nodal coordinates of the bone and the cartilage thickness per subchondral node to produce a statistical model of the cartilage thickness and bone shape. A 3D scatter of principal components 1, 2, and 4, of the training set was plotted by sex (F), showing the decision boundary plane (grey) of the logistic regression model, and a vector (green) that passes through the average male and female knee.