Wavelet method for CT colonography computer-aided polyp detection

Abstract

Computed tomographic colonography (CTC) computer aided detection (CAD) is a new method to detect colon polyps. Colonic polyps are abnormal growths that may become cancerous. Detection and removal of colonic polyps, particularly larger ones, has been shown to reduce the incidence of colorectal cancer. While high sensitivities and low false positive rates are consistently achieved for the detection of polyps sized 1 cm or larger, lower sensitivities and higher false positive rates occur when the goal of CAD is to identify "medium"-sized polyps, 6-9 mm in diameter. Such medium-sized polyps may be important for clinical patient management. We have developed a wavelet-based postprocessor to reduce false positives for this polyp size range. We applied the wavelet-based postprocessor to CTC CAD findings from 44 patients in whom 45 polyps with sizes of 6-9 mm were found at segmentally unblinded optical colonoscopy and visible on retrospective review of the CT colonography images. Prior to the application of the wavelet-based postprocessor, the CTC CAD system detected 33 of the polyps (sensitivity 73.33%) with 12.4 false positives per patient, a sensitivity comparable to that of expert radiologists. Fourfold cross validation with 5000 bootstraps showed that the wavelet-based postprocessor could reduce the false positives by 56.61% (p <0.001), to 5.38 per patient (95% confidence interval [4.41, 6.34]), without significant sensitivity degradation (32/45, 71.11%, 95% confidence interval [66.39%, 75.74%], p=0.1713). We conclude that this wavelet-based postprocessor can substantially reduce the false positive rate of our CTC CAD for this important polyp size range.

Figure 1

A block diagram representation of our CTC CAD system and the proposed wavelet-based postprocessor for false positive reduction. The proposed method is shown as the second classifier above.

Figure 2

Left: one original true polyp image. Right: wavelet decomposed images (5 level), where V_i, D_i, and H_i denote the vertical, horizontal, and diagonal directions at level i.

Figure 3

Process for taking snapshots of polyp detections. The camera and lighting source locations are adjusted to maximize polyp surface information in the resulting image.

Figure 4

Viewpoint entropy of one object with a 3D mesh representation, where the lower part is the projection result of the 3D mesh under the specified projection direction.

Figure 5

The search path to efficiently locate a good viewpoint. The dotted line represents the colon centerline, C is the polyp centroid, D is the nearest centerline point to C, and P_N is the average normal of the polyp surface. We first locate D and extend D to B and E, the beginning and end search points. We then perform a coarse search for the optimal centerline viewpoint P_C between B and E. Finally, we refine P_c to P_f using a higher resolution search.

Figure 6

Algorithm outline for locating the optimal viewpoint along the colon centerline.

Figure 7

Viewpoint entropy calculation. C is the polyp centroid, Pc is a viewpoint which determines the projection direction, V_i represents one vertex on the mesh and F_i is one face in the mesh.

Figure 8

Camera parameters setting.

Figure 9

FROC curve of our CTC CAD system before application of the wavelet-based postprocessor.

Figure 10

CTC CAD detection samples: Polyp images.

Figure 11

CTC CAD detection samples: False positive images.

Figure 12

FROC curve for the operating point 1 in Fig. 9 following false positive reduction using the wavelet-based postprocessor. B, C and D are three chosen points that will be further analyzed in the bootstrap experiment (see Fig. 14).

Figure 13

FROC curve for the operating point 2 in Fig. 9 following false positive reduction using the wavelet-based postprocessor. A is the chosen point that will be further analyzed in the bootstrap experiment (see Fig. 14).

Figure 14

Original FROC and bootstrap results for the four chosen operating points. The length of the error bars are twice the standard deviation associated with the corresponding operation point. Point A was chosen on the FROC in Fig. 13 and points B, C, D were chosen on the FROC curve in Fig. 12.

Figure 15

A 6 mm polyp with the lowest score (0.09) given by the committee classifier in the wavelet-based postprocessing experiment for the chosen point A in Fig. 13. There was a hole in the colon surface due to poor colon surface segmentation. A score close to “0” indicates a detection that is unlikely to be a polyp while a score close to “1” denotes a detection that is very likely to be a true polyp.

Figure 16

Three polyps having relatively low scores in the wavelet-based postprocessing experiment for the chosen point A in Fig. 13. Polyps touched (a) a fold, (b) an air-fluid boundary, or (c) shadows.