Automated Scheme for Measuring Polyp Volume in CT Colonography Using Hessian Matrix-Based Shape Extraction and 3D Volume Growing

Current measurement of the single longest dimension of a polyp is subjective and has variations among radiologists. Our purpose was to develop an automated measurement of polyp volume in CT colonography (CTC). We developed a computerized segmentation scheme for measuring polyp volume in CTC, which consisted of extraction of a highly polyp-like seed region based on the Hessian matrix, segmentation of polyps by use of a 3D volume-growing technique, and sub-voxel refinement to reduce a bias of segmentation. Our database consisted of 30 polyp views (15 polyps) in CTC scans from 13 patients. To obtain "gold standard," a radiologist outlined polyps in each slice and calculated volumes by summation of areas. The measurement study was repeated three times at least one week apart for minimizing a memory effect bias. We used the mean volume of the three studies as "gold standard." Our measurement scheme yielded a mean polyp volume of 0.38 cc (range: 0.15-1.24 cc), whereas a mean "gold standard" manual volume was 0.40 cc (range: 0.15-1.08 cc). The mean absolute difference between automated and manual volumes was 0.11 cc with standard deviation of 0.14 cc. The two volumetrics reached excellent agreement (intra-class correlation coefficient was 0.80) with no statistically significant difference (p(F≤f) = 0.42). Thus, our automated scheme efficiently provides accurate polyp volumes for radiologists.