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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. Index Terms--Centerline, colon, level sets, virtual colonoscopy (vc).

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]

Purpose: In computed tomographic colonography (CTC), a patient will be scanned twice—Once supine and once prone—to improve the sensitivity for polyp detection. To assist radiologists in CTC reading, in this paper we propose an automated method for colon registration from supine and prone CTC scans. Methods: We propose a new colon centerline registration method for prone and supine CTC scans using correlation optimized warping (COW) and canonical correlation analysis (CCA) based on the anatomical structure of the colon. Four anatomical salient points on the colon are first automatically distinguished. Then correlation optimized warping is applied to the segments defined by the anatomical landmarks to improve the global registration based on local correlation of segments. The COW method was modified by embedding canonical correlation analysis to allow multiple features along the colon centerline to be used in our implementation. Results: We tested the COW algorithm on a CTC data set of 39 patients with 39 polyps (19 training and 20 test cases) to verify the effectiveness of the proposed COW registration method. Experimental results on the test set show that the COW method significantly reduces the average estimation error in a polyp location between supine and prone scans by 67.6%, from 46.27±52.97 to 14.98 mm±11.41 mm, compared to the normalized distance along the colon centerline algorithm (p<0.01). Conclusions: The proposed COW algorithm is more accurate for the colon centerline registration compared to the normalized distance along the colon centerline method and the dynamic time warping method. Comparison results showed that the feature combination of z-coordinate and curvature achieved lowest registration error compared to the other feature combinations used by COW. The proposed method is tolerant to centerline errors because anatomical landmarks help prevent the propagation of errors across the entire colon centerline. [ABSTRACT FROM AUTHOR]

A multiobjective genetic algorithm is designed to optimize a computer-aided detection (CAD) system for identifying colonic polyps. Colonic polyps appear as elliptical protrusions on the inner surface of the colon. Curvature-based features for colonic polyp detection have proved to be successful in several CT colonography (CTC) CAD systems. Our CTC CAD program uses a sequential classifier to form initial polyp detections on the colon surface. The classifier utilizes a set of thresholds on curvature-based features to cluster suspicious colon surface regions into polyp candidates. The thresholds were previously chosen experimentally by using feature histograms. The chosen thresholds were effective for detecting polyps sized 10 mm or larger in diameter. However, many medium-sized polyps, 6–9 mm in diameter, were missed in the initial detection procedure. In this paper, the task of finding optimal thresholds as a multiobjective optimization problem was formulated, and a genetic algorithm to solve it was utilized by evolving the Pareto front of the Pareto optimal set. The new CTC CAD system was tested on 792 patients. The sensitivities of the optimized system improved significantly, from 61.68% to 74.71% with an increase of 13.03% (95% CI [6.57%, 19.5%], p=7.78×10-5) for the size category of 6–9 mm polyps, from 65.02% to 77.4% with an increase of 12.38% (95% CI [6.23%, 18.53%], p=7.95×10-5) for polyps 6 mm or larger, and from 82.2% to 90.58% with an increase of 8.38% (95%CI [0.75%, 16%], p=0.03) for polyps 8 mm or larger at comparable false positive rates. The sensitivities of the optimized system are nearly equivalent to those of expert radiologists. [ABSTRACT FROM AUTHOR]

A multiobjective genetic algorithm is designed to optimize a computer-aided detection (CAD) system for identifying colonic polyps. Colonic polyps appear as elliptical protrusions on the inner surface of the colon. Curvature-based features for colonic polyp detection have proved to be successful in several CT colonography (CTC) CAD systems. Our CTC CAD program uses a sequential classifier to form initial polyp detections on the colon surface. The classifier utilizes a set of thresholds on curvature-based features to cluster suspicious colon surface regions into polyp candidates. The thresholds were previously chosen experimentally by using feature histograms. The chosen thresholds were effective for detecting polyps sized 10 mm or larger in diameter. However, many medium-sized polyps, 6-9 mm in diameter, were missed in the initial detection procedure. In this paper, the task of finding optimal thresholds as a multiobjective optimization problem was formulated, and a genetic algorithm to solve it was utilized by evolving the Pareto front of the Pareto optimal set. The new CTC CAD system was tested on 792 patients. The sensitivities of the optimized system improved significantly, from 61.68% to 74.71% with an increase of 13.03% (95% CI [6.57%, 19.5%], p = 7.78 x 10(-5)) for the size category of 6-9 mm polyps, from 65.02% to 77.4% with an increase of 12.38% (95% CI [6.23%, 18.53%], p = 7.95 x 10(-5)) for polyps 6 mm or larger, and from 82.2% to 90.58% with an increase of 8.38% (95% CI [0.75%, 16%], p = 0.03) for polyps 8 mm or larger at comparable false positive rates. The sensitivities of the optimized system are nearly equivalent to those of expert radiologists.

Objective: The purpose of this study was to investigate the variability of CT colonography (CTC) scan quality obtained within and between institutions by using previously validated automated quality assessment (QA) software that assesses colonic distention and surface area obscured by residual fluid., Materials and Methods: The CTC scans of 120 patients were retrospectively selected, 30 from each of four institutions. The bowel preparation included oral contrast material for fecal and fluid tagging. Patients at one institution (institution 4) drank half the amount of oral contrast material compared with the patients at the other three institutions. Fifteen of the CTC scans were from the beginning of the protocol studied at each institution and 15 scans were from the same protocol acquired approximately 1 year later in the study. We used previously validated QA software to automatically measure the mean distention and residual fluid of each of five colonic segments (ascending, transverse, descending, sigmoid, and rectum). Adequate distention was defined as a colonic diameter of at least 2 cm. Residual fluid was determined by the percentage of colonic surface area covered by fluid. We compared how the quality varied across multiple institutions and over time within the same institution., Results: No significant difference in the amount of colonic distention among the four institutions was found (p = 0.19). However, the distention in the prone position was significantly greater than the distention in the supine position (p < 0.001). Patients at institution 4 had about half the amount of residual colonic fluid compared with patients at the other three institutions (p < 0.01). The sigmoid and descending colons were the least distended segments, and the transverse and descending colons contained the most fluid on the prone and supine scans, respectively. More recently acquired studies had greater distention and less residual fluid, but the differences were not statistically significant (p = 0.30 and p = 0.96, respectively)., Conclusion: Across institutions, a significant difference can exist in bowel preparation quality for CTC. This study reaffirms the need for standardized bowel preparation and quality monitoring of CTC examinations to reduce poor CTC performance.

Objective: A computer-aided detection (CAD) system with high sensitivity in the detection of adenomatous polyps in varied CT colonography (CTC) data sets increases the utility of CAD in the clinical setting. The purpose of this study was to evaluate the standalone performance of an existing CAD system with a new set of CTC data from screening patients at an institution and geographic location different from those at which the CAD system was trained., Materials and Methods: CTC data were collected from the records of 104 patients undergoing screening for colorectal neoplasia. Most of the patients were at average risk, had CTC findings suggestive of polyps, and underwent colonoscopy. Patients underwent cathartic bowel preparation, were given an oral contrast agent, and underwent imaging in the prone and supine positions. The patients had 86 adenomas confirmed at same-day optical colonoscopy; 47 of these tumors were 10 mm in diameter or larger, and 39 measured 6-9 mm. The CTC data were analyzed with an existing CAD system for colonography that was trained with previously acquired data. In a previous non-polyp-enriched screening cohort, the standalone performance of the CAD system was 93.3% (28/30) sensitivity for adenomatous polyps 10 mm or larger, 51.1% (47/92) sensitivity for adenomas 6-9 mm, and a mean false-positive rate of 8.6 per patient. Sensitivity comparisons were made with findings in the previous study., Results: The CAD system had per-polyp sensitivities of 91.5% (43/47; 95% CI, 78.7-97.2%; p = 1.0) for adenomas 10 mm or larger and 82.1% (32/39; 65.9-91.9%; p = 0.0009) for adenomas 6-9 mm. The per-patient sensitivities were 97.6% (40/41; 85.6-99.9%; p = 0.6) for patients with adenomas 10 mm or larger and 82.4% (28/34; 64.8-92.6%; p = 0.047) for patients with adenomas 6-9 mm. The mean and median false-positive rates were 9.6 +/- 9.6 and 7.0 per patient, respectively. Common reasons for CAD misses (false-negative findings) were the presence of adherent contrast medium, flat adenomas, and adenomas located on or adjacent to normal colonic folds. In a random sample, 72.5% (29/40) of false-positive findings were attributable to folds or residual feces., Conclusion: The CAD system evaluated has a high level of performance in the detection of adenomatous polyps with CTC data from a polyp-enriched cohort different from that used to train the system.

This retrospective study investigates the potential of image analysis to quantify for the presence and extent of pulmonary hypertension secondary to sickle cell disease (SCD). A combination of fast marching and geodesic active contours level sets were employed to segment the pulmonary artery from smoothed CT-Angiography images from 16 SCD patients and 16 matching controls. An algorithm based on fast marching methods was used to compute the centerline of the segmented arteries to measure automatically the diameters of the pulmonary trunk and first branches of the pulmonary arteries. Results show that the pulmonary trunk and arterial branches are significantly larger in diameter in SCD patients as compared to controls (p-values of 0.002 for trunk and 0.0003 for branches). For validation, the results were compared with manually measured values and did not demonstrate significant difference (mean p-values 0.71). CT with image processing shows great potential as a surrogate indicator of pulmonary hemodynamics or response to therapy, which could be an important tool for drug discovery and noninvasive clinical surveillance.

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.