Texture-based CAD improves diagnosis for low-dose CT colonography

Computed tomography (CT)-based virtual colonoscopy or CT colonography (CTC) currently utilizes oral contrast solutions to tag the colonic fluid and possibly residual stool for differentiation from the colon wall and polyps. The enhanced image density of the tagged colonic materials causes a significant partial volume (PV) effect into the colon wall as well as the lumen space (filled with air or CO₂). The PV effect on the colon wall can "bury" polyps of size as large as 5mm by increasing their image densities to a noticeable level, resulting in false negatives. It can also create false positives when PV effect goes into the lumen space. We have been modeling the PV effect for mixture-based image segmentation and developing text-based computer-aided detection of polyp (CADpolyp) by utilizing the PV mixture-based image segmentation. This work presents some preliminary results of developing and applying texture-based CADpolyp technique to low-dose CTC studies. A total of 114 studies of asymptomatic patients older than 50, who underwent CTC and then optical colonoscopy (OC) on the same day, were selected from a database, which was accumulated in the past decade and contains various bowel preparations and CT scanning protocols. The participating radiologists found ten polyps of greater than 5 mm from a total of 16 OC proved polyps, i.e., a detection sensitivity of 63%. They scored 23 false positives from the database, i.e., a 20% false positive rate. Approximately 70% of the datasets were marked as imperfect bowel cleansing and/or presence of image artifacts. The impact of imperfect bowel cleansing and image artifacts on VC performance is significant. The texture-based CADpolyp detected all the polyps with an average of 2.68 false positives per patient. This indicates that texture-based CADpolyp can improve the CTC performance in the cases of imperfect cleansed bowels and presence of image artifacts.