Evaluation of a semi-automatic segmentation of cartilage based on k-means (2003)

J. Carballido-Gamio, T. C. Dunn, G. E. Lau, S. Banerjee, S. Majumdar University of California, San Francisco, San Francisco, California, United States, University of California, Berkeley, Berkeley, California, United States Synopsis Cartilage segmentation based on magnetic resonance images (MRI) is a necessary but often time-consuming process in order to determine morphologic parameters for assessment of osteoarthritis (OA) of the knee. A semi-automatic segmentation process was developed and shown to reduce the number of points placed by 35% on average compared to manual segmentation. The paired (n = 268) volume and thickness values for the two techniques were found to have good agreement (r = 0.932 and r = 0.851, respectively), however there were a significant increases in both with the semi-automatic technique of 35% (p<0.001, both). Introduction Osteoarthritis (OA) of the knee has been successfully assessed by magnetic resonance imaging, providing insight to the biochemical and morphological changes in the cartilage with progression of the disease. In order to quantify morphological features of the cartilage, the cartilage region must be segmented from the acquired MR image. While MR sequence parameters can provide excellent discrimination of the cartilage, automatic segmentation of the cartilage has been a challenge to date. We developed and assessed the performance of a semi-automatic segmentation process against manual cartilage segmentation Materials and Methods Sagittal 3D SPGR MR images of the knee of 6 subjects were acquired at 1.5T (Signa scanner; GE Medical Systems, Milwaukee, WI) using a phased array surface coil. Images were acquired with the following parameters applying fat saturation: TI/TR/TE/o = 8/ 30 / 3.3 ms/30o, 60 slices, slice thickness = 2mm, matrix size = 512x512, with in-plane resolution of 0.235 mm. Images were coil corrected with an in-house algorithm and in-plane radio-frequency (RF) corrected with an intensitybased RF correction technique [1]. In order to increase brightness homogeneity, the anisotropic-diffusion algorithm [2] was applied followed by isotropic dilation in gray scale for shape preservation. Concentration, a linguistic hedge borrowed from fuzzy logic theory [3], was applied as a final pre-processing step before clustering the data with k-means [4]. Three different masks, that we called “pre-masks”, were generated from the clusters obtained by k-means. Based on a priori knowledge that cartilage is the brightest “group” in the MR image acquired with the parameters mentioned above, the first pre-mask was the set of pixels corresponding to the brightest center of the clustered data. The second pre-mask was equal to the first one plus the set of pixels corresponding to the second brightest center, and same reasoning for the third pre-mask. All the previous steps were done automatically. In order to get a final mask representing only cartilage a graphical user interface was developed, which let the user choose the pre-mask and place points for segmentation based on splines. Cartilage thickness and volume of the medial femoral cartilage were determined for 3 normals and 3 patients with severe osteoarthritis by 4 operators by both the manually and semi-automatic techniques, providing 268 paired cartilage segementations. These were processed by custom software to determine volume and thickness measures. The number of points placed for manual segmentation was compared with the number of points placed for pre-masks refining (the “semi-automatic” method). Comparisons of the mean number of points placed, thickness and volume were tested for significance with a paired t-test. Thickness and volume pairs were compared with linear regression analysis to evaluate systematic trends. Results On average, 56 + 31 (mean + 1 SD) points were placed per slice to segment the femoral cartilage with the manual process, compared to 36 + 21 (mean + 1 SD) points for the semi-automatic process -an average reduction of 35% -and was significantly different (p < 0.01). Half of the slices were found to have less than 65% of the number of manual points when segmented with the semi-automatic process, and three-fourths of the slices had less than 85% of the number of manual points. 15% of the slices had the same or more points placed with the semi-automatic technique compared to the manual method. In general, the reduction in points is on the proximal surface of the cartilage, at the bone-cartilage interface, where the contrast is easily determined by the semi-automatic technique. The mean medial femoral cartilage thickness and volume were found to be significantly larger for the semi-automatic technique (p < 0.01). The thickness and volumes were 0.82 + 0.50 mm and 147.6 + 110.8 mm (mean + 1 SD) for the manual segmentation, compared to 1.14 + 0.54 and 198.0 + 123.0 mm (mean + 1 SD) and for the semi-automatic segmentation. The linear regression equation of the semi-automatic volume (y) versus the manual volume (x) was found to be y=1.03x + 45.3 (r = 0.932), and for thickness y=0.93x + 0.37 (r = 0.851). Discussion The increased volume and thickness of the cartilage segmented by the semi-automatic process was evaluated closely. In most cases, the increased thickness (and therefore volume) was seen in the posterior aspect of the femoral condyle, between the cartilage and adjacent fluid, fat, and muscle. This region tends to have the least contrast between the cartilage and the surrounding tissue, and the most basic explanations for the discrepancy include not being able to see contrast with manual segmentation, or accepting inclusion of non-cartilage regions with the semi-automatic technique. It is important that for diagnostic and longitudinal progression studies, the bias could be corrected with appropriate validation of cartilage segmentation, or simply adjusted empirically.