Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps.
We have developed a three-dimensional (3-D) computer-aided diagnosis scheme for automated detection of colonic polyps in computed tomography (CT) colonographic data sets, and assessed its performance based on colonoscopy as the gold standard. In this scheme, a thick region encompassing the entire colonic wall is extracted from an isotropic volume reconstructed from the CT images in CT colonography. Polyp candidates are detected by first computing of 3-D geometric features that characterize polyps, folds, and colonic walls at each voxel in the extracted colon, and then segmenting of connected components corresponding to suspicious regions by hysteresis thresholding based on these geometric features. We apply fuzzy clustering to these connected components to obtain the polyp candidates. False-positive (FP) detections are then reduced by computation of several 3-D volumetric features characterizing the internal structures of the polyp candidates, followed by the application of discriminant analysis to the feature space generated by these volumetric features. The locations of the polyps detected by our computerized method were compared to the gold standard of conventional colonoscopy. The performance was evaluated based on 43 clinical cases, including 12 polyps determined by colonoscopy. Our computerized scheme was shown to have the potential to detect polyps in CT colonography with a clinically acceptable high sensitivity and a low FP rate.