Fig 1.
Surface partial volume effect.
When imaging a real-world object (green outline) with a limited spatial resolution, pixels on the surface contain a mixture of object and the surrounding (“partial volume effect”). This effect is further enhanced by additional blurring inherent to CT scans. We introduced an approach where the objects are segmented by first using a relatively low HU threshold, and subtracting surface pixels (red) multiplied with a global factor, see text.
Fig 2.
Segmentation-derived 3D-volume rendering and photograph of an urolith.
The basic shape of the urolith is reproduced accurately, but the fine structure of the surface is not comprehensible in the 3D representation.
Table 1.
Parameters and correlations coefficients for the four different algorithms.
Fig 3.
Correlation plots of estimated vs. reference standard diameters (A, B, E, F, I, J, M, N) and volumes (C, D, G, H, K, L, O, P) for all four segmentation approaches (A-D, E-H, I-L, M-P).
The plots were created with all 101 uroliths using the optimal average parameters from the repeated optimization and validation procedure (see Table 1). Obviously, all approaches yield similar results, with good correlations.
Fig 4.
Bland-Altman plots of estimated vs. reference standard diameters (A, B, E, F, I, J, M, N) and volumes (C, D, G, H, K, L, O, P) for all four segmentation approaches (A-D, E-H, I-L, M-P).
Dashed lines in Bland-Altman plots correspond to mean and 1.96* standard deviation. The plots were created with all 101 uroliths using the optimal average parameters from the repeated optimization and validation procedure (see Table 1). Obviously, all approaches yield similar results, with comparable mean differences and standard deviations.