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Computer-aided diagnosis (CAD) systems must show sufficient versatility to produce robust analysis on a large variety of data. In the case of colonography, CAD has not been designed to cope with the presence of stool, although labeling the stool with high contrast agents replaces the use of laxatives and reduces the patient discomfort. This procedure introduces additional challenges for the diagnosis, such as poorly tagged stool, stool sticking to colonic walls, and heterogeneous stool (tagged stool mixed with air or untagged stool). Our study proposes a robust algorithm for heterogeneous stool removal to be employed as a preprocessing module for CAD systems in colonic cancer detection. Colonoscopy data are automatically cleansed of residual stool to enhance the polyp appearance for improved diagnosis. The algorithm uses expectation-maximization, quadratic regression, level sets and minimum variance. Results show stool removal accuracy on polyps which are partially or fully covered by stool. The results are robust on stool lining and large pools of heterogeneous and weakly-tagged stool. The automatic detection of colon polyps using our CAD system on cathartic-free data improves considerably with the addition of the automatic stool removal module from 74% to 86% true positive (TP) rate at 6.4 false positives (FP)/case.

Objective: The purpose of this study was to validate automated quality assessment (QA) software for CT colonography (CTC) by comparing results obtained with the software with results of interpretation by radiologists in the assessment of colonic distention and surface area obscured by residual fluid., Materials and Methods: CTC scans of 30 patients were selected retrospectively to span ranges of luminal distention (well distended to poorly distended) and surface area covered by residual fluid (high amount of coverage to low amount of coverage). We used QA software developed in our laboratory to automatically measure the mean distention of each of five colonic segments (ascending, transverse, descending, sigmoid, and rectum). Three experienced radiologists visually graded each scan for distention and fluid coverage. Distention and fluid scores for specific segments were assessed with Bland-Altman analysis (mean difference with 95% limits of agreement) and the weighted kappa test. Interobserver and intraobserver variability was determined with the weighted kappa test., Results: For distention scoring, the mean difference between radiologists and the QA software was 0.1% (95% limits of agreement, -25.6% and 25.9%). For fluid scoring, the mean difference was -0.6% (95% limits of agreement, -8.2% and 7.1%). There was moderate to good agreement (weighted kappa value, 0.50-0.78) between the radiologists' mean scores and the scores obtained with the QA software and for interreader and intrareader assessments of distention and fluid coverage., Conclusion: Results with the QA software agreed with radiologists' assessment of colonic distention and residual fluid coverage but were a more objective assessment. Use of this QA software can help standardize two important factors, distention and residual fluid coverage, that affect the quality of CTC, reducing two known causes of poor CTC performance.

Virtual colonoscopy (VC) is becoming a more prevalent method to detect and diagnose colorectal cancer. An essential component of using VC to detect cancerous polyps, especially in conjunction with computer-aided diagnosis, is the accurate calculation of the centerline of the colon. While the colon is often modeled as a simple cylinder, the amount of colonic distention may vary between patients and within the same patient often causing loops and multiple disconnected segments to be present in the colon segmentation. These variations have caused previous centerline algorithms to fail to capture a complete and accurate centerline for all colons. We have developed an automatic method to determine from a computed tomography (CT) VC a subvoxel precise centerline that is accurate even in cases of over-distended or under-distended colons. In this algorithm, the loops in the colon caused by over-distention are detected and removed when the centerline calculation is performed. Also, a newly developed method for the detection and segmentation of the outer wall of the colon is used to connect collapsed portions of the colon where the lumen segmentation fails to produce a continuous centerline. These two methods allow for a complete and accurate centerline to be calculated in uniformly distended colons as well as in colons containing segments which are over-distended and/or under-distended. We have demonstrated successfully the effectiveness of our algorithm on 50 cases, 25 of which resulted in erroneous solutions by previous centerline algorithms due to variability in the colon distention.

In this paper, we propose a new registration method for prone and supine computed tomographic colonography scans using graph matching. We formulate 3-D colon registration as a graph matching problem and propose a new graph matching algorithm based on mean field theory. In the proposed algorithm, we solve the matching problem in an iterative way. In each step, we use mean field theory to find the matched pair of nodes with highest probability. During iterative optimization, one-to-one matching constraints are added to the system in a step-by-step approach. Prominent matching pairs found in previous iterations are used to guide subsequent mean field calculations. The proposed method was found to have the best performance with smallest standard deviation compared with two other baseline algorithms called the normalized distance along the colon centerline (NDACC) ( p = 0.17) with manual colon centerline correction and spectral matching ( p < 1e−5). A major advantage of the proposed method is that it is fully automatic and does not require defining a colon centerline for registration. For the latter NDACC method, user interaction is almost always needed for identifying the colon centerlines. [ABSTRACT FROM PUBLISHER]