Improving initial polyp candidate extraction for CT colonography

Abstract

Reducing the number of false positives (FPs) as much as possible is a challenging task for computer-aided detection (CAD) of colonic polyps. As part of a typical CAD pipeline, an accurate and robust process for segmenting initial polyp candidates (IPCs) will significantly benefit the successive FP reduction procedures, such as feature-based classification of false and true positives (TPs). In this study, we introduce an improved scheme for segmenting IPCs. It consists of two main components. One is geodesic distance-based merging, which merges suspicious patches (SPs) for IPCs. Based on the merged SPs, another component, called convex dilation, grows each SP beyond the inner surface of the colon wall to form a volume of interest (VOI) for that IPC, so that the inner border of the VOI beyond the colon inner surface could be segmented as convex, as expected. The IPC segmentation strategy was evaluated using a database of 50 patient studies, which include 100 scans at supine and prone positions with 84 polyps and masses sized from 6 to 35 mm. The presented IPC segmentation strategy (or VOI extraction method) demonstrated improvements, in terms of having no undesirably merged true polyp and providing more helpful mean and variance of the image intensities rooted from the extracted VOI for classification of the TPs and FPs, over two other VOI extraction methods (i.e. the conventional method of Nappi and Yoshida (2003 Med. Phys. 30 1592-601) and our previous method (Zhu et al 2009 Cancer Manag. Res. 1 1-13). At a by-polyp sensitivity of 0.90, these three methods generated the FP rate (number of FPs per scan) of 4.78 (new method), 6.37 (Nappi) and 7.01 (Zhu) respectively.

Fig. 1

The outline of a CAD scheme to evaluate the presented two strategies.

Fig. 2

The endoluminal or endoscopic view of a 35 mm lobulated tubulovillous adenoma near the rectum of a 54-year old female. The three arrows indicate three separated SPs, respectively, which are detected for this single polyp due to the three lobules.

Fig. 3

(a) The endoluminal view of a 9 mm polyp and stool which are close to each other. (b) A 6 mm polyp and stool in the 2D axial slice. Obviously, they are located in different segments of the colon. (c) The endoluminal view of the two colonic objects in (b), where the view angle is set mainly for the polyp. (d) Similar to (c), but at another view angle for the stool.

Fig. 4

The two dark circles represent two major objects, on which there are two bumps (mimicking polypoid-like objects) in light gray respectively. Parts of the bumps encompassed by the red curves are the peak and neck areas of the objects, denoted by P1 to P4, which represent the SPs detected by an analysis on each voxel on the borders of the major objects.

Fig. 5

A flow chart for the GDM strategy.

Fig. 6

Voxel pairs on the geodesic path.

Fig. 7

Illustration of the CD strategy in 2D case. The dilation process proceeds from the green area (pixels belong to V_n) to the gray area (pixels in tissue area outside V_n). Pixels in cyan represent the neighbors of V_n.

Fig. 8

The incidences of UMPs and WMPs according to the threshold γ.

Fig. 9

The fROC curves of the testing procedures. The three curves depict the performance of the three VOI extraction methods using the two features. The transparent rectangle at each point on the three curves denotes the confidence intervals (95% confidence level) for the sensitivity and FP rate.

Fig. 7

An experimental result of the CD strategy. (a) A 12mm sessile polyp in an axial slice, where the voxels in red indicate the SP. (b) The generated initial VOI V₀ in the slice (including the red and green area). (c) The result of the previous method in [9]. (d) The result of the previous method in [14], where the blue curve represents the inner border found by the Harr transformation-based edge finder. (e) The result of the presented CD strategy with r=9 and α =0.9.