Centerline-based colon segmentation for CT colonography

We have developed a fully automated algorithm for colon segmentation, centerline-based segmentation (CBS), which is faster than any of the previously presented segmentation algorithms, but also has high sensitivity as well as high specificity. The algorithm first thresholds a set of unprocessed CT slices. Outer air is removed, after which a bounding box is computed. A centerline is computed for all remaining regions in the thresholded volume, disregarding segments related to extracolonic structures. Centerline segments are connected, after which the anatomy-based removal of segments representing extracolonic structures occurs. Segments related to the remaining centerline are locally region grown, and the colonic wall is found by dilation. Shape-based interpolation provides an isotropic mask. For 38 CT datasets, CBS was compared with the knowledge-guided segmentation (KGS) algorithm for sensitivity and specificity. With use of a 1.5 GHz AMD Athlon-based PC, the average computation time for the segmentation was 14.8 s. The sensitivity was, on average, 96%, and the specificity was 99%. A total of 21% of the voxels segmented by KGS, of which 96% represented extracolonic structures and 4% represented the colon, were removed.