An abdominal computed tomographic scan was modified by inserting 10 simulated colonic polyps with use of methods that closely mimic the attenuation, noise, and polyp-colon wall interface of naturally occurring polyps. A shape-based polyp detector successfully located six of the 10 polyps. When settings that enhanced the edge profile of polyps were chosen, eight of 10 polyps were detected. There were no false-positive detections. Shape analysis is technically feasible and is a promising approach to automated polyp detection.
The American Cancer Society estimates that 10% of new cancer cases and cancer deaths in 1999 will be due to cancer of the colon and rectum (1). Many of these cases can be prevented if precursor malignant colonic polyps are detected early and removed. Currently, the best technique for detection of colonic polyps is colonoscopy. Computed tomographic (CT) colonography is a new method for detection of colonic polyps that is now undergoing evaluation at a number of reasearch hospitals (2-5). In contrast to colonoscopy, CT colonography is less invasive and does not require sedation.
The appropriate way to perform and interpret CT colonographic studies is still in evolution. Findings in a preliminary clinical study suggest that optimal interpretation consists primarily of using the two-dimensional images supplemented where needed with analysis of three-dimensional (virtual colonoscopic) reconstructions (6). Other work suggests that three-dimensional views may increase detection when used either alone (7) or in combination with two-dimensional images (8). Because a typical CT colonographic study consists of many CT scans (300-600 for supine and prone studies depending on technique), it is time-consuming to interpret (9). There is also a need to improve the sensitivity of CT colonography, which in preliminary reports is 75%-83% for polyps 8-10 mm in diameter or larger (6,10). We hypothesized that computer-assisted polyp detection could potentially improve efficiency of interpretation and increase sensitivity. For these reasons, we developed a computer-assisted detection algorithm and tested it in an established phantom model for colonic polyps