Centerline-based colon segmentation for CAD of CT colonography

We developed a fast centerline-based segmentation (CBS) algorithm for the extraction of colon in computer-aided detection (CAD) for CT colonography (CTC). CBS calculates local centerpoints along thresholded components of abdominal air, and connects the centerpoints iteratively to yield a colon centerline. A thick region encompassing the colonic wall is extracted by use of region-growing around the centerline. The resulting colonic wall is employed in our CAD scheme for the detection of polyps, in which polyps are detected within the wall by use of volumetric shape features. False-positive detections are reduced by use of a Bayesian neural network. The colon extraction accuracy of CBS was evaluated by use of 38 clinical CTC scans representing various preparation conditions. On average, CBS covered more than 96% of the visible region of colon with less than 1% extracolonic components in the extracted region. The polyp detection performance of the CAD scheme was evaluated by use of 121 clinical cases with 42 colonoscopy-confirmed polyps 5-25 mm. At a 93% by-polyp detection sensitivity for polyps >5 mm, a leave-one-patient-out evaluation yielded 1.4 false-positive polyp detections per CT scan.