CT Colonography Computer-aided Detection Research Articles

Computer-aided detection for CT colonography

CT colonography (CTC) is a non-invasive screening technique for the detection of colorectal polyps, as an alternative to optical colonoscopy in clinical practice. Computer-aided detection (CAD) for CTC refers to a scheme which automatically detects colorectal polyps and masses in CT images of the colon. It has the potential to increase radiologists’ detection performance and greatly shorten the detection time. Over the years, technical developments have advanced CAD for CTC substantially. In this paper, key techniques used in CAD for polyp detection are reviewed. Illustrations about the performance of existing CAD schemes show their relatively high sensitivity and low false positive rate. However, these CAD schemes are still suffering from technical or clinical problems. Some existing challenges faced by CAD are also pointed out at the end of this paper.

Comparative performance of a primary-reader and second-reader paradigm of computer-aided detection for CT colonography in a low-prevalence screening population

To compare the efficacy of computer-aided detection (CAD) for computed tomographic colonography (CTC) when employed as either primary-reader or second-reader paradigms in a low-prevalence screening population. Ninety screening patients underwent same-day CTC and colonoscopy. Four readers prospectively interpreted all CTC data sets using a second-reader paradigm (unassisted interpretation followed immediately by CAD assistance). Three months later, randomized anonymous data sets were re-interpreted by all readers using a primary-reader paradigm (only CAD prompts evaluated). Compared with the average per-patient sensitivity for unassisted interpretation (0.57), both CAD paradigms significantly increased sensitivity: 0.78 (p < 0.001) for the second-reader paradigm and 0.83 (p < 0.001) for the primary-reader paradigm. There was no significant difference between CAD paradigms (p = 0.25). The average per-patient specificity for polyps ≥6 mm was significantly higher using the primary-reader paradigm than the second-reader paradigm (0.90 vs. 0.83, respectively, p = 0.006), with ROC AUCs of 0.83 and 0.68, respectively. Reading time using CAD as a primary-reader paradigm (median 1.4 min) was significantly shorter than both unassisted (median 4.0 min, p < 0.001) and second-reader paradigms (median 5.5 min, p < 0.001). CAD improves radiologist sensitivity in screening patients when used as either a second- or primary-reader paradigm, although the latter may improve specificity and efficiency more.

Strategies for improved interpretation of computer-aided detections for CT colonography utilizing distributed human intelligence

Computer-aided detection (CAD) systems have been shown to improve the diagnostic performance of CT colonography (CTC) in the detection of premalignant colorectal polyps. Despite the improvement, the overall system is not optimal. CAD annotations on true lesions are incorrectly dismissed, and false positives are misinterpreted as true polyps. Here, we conduct an observer performance study utilizing distributed human intelligence in the form of anonymous knowledge workers (KWs) to investigate human performance in classifying polyp candidates under different presentation strategies. We evaluated 600 polyp candidates from 50 patients, each case having at least one polyp ≥6 mm, from a large database of CTC studies. Each polyp candidate was labeled independently as a true or false polyp by 20 KWs and an expert radiologist. We asked each labeler to determine whether the candidate was a true polyp after looking at a single 3D-rendered image of the candidate and after watching a video fly-around of the candidate. We found that distributed human intelligence improved significantly when presented with the additional information in the video fly-around. We noted that performance degraded with increasing interpretation time and increasing difficulty, but distributed human intelligence performed better than our CAD classifier for "easy" and "moderate" polyp candidates. Further, we observed numerous parallels between the expert radiologist and the KWs. Both showed similar improvement in classification moving from single-image to video interpretation. Additionally, difficulty estimates obtained from the KWs using an expectation maximization algorithm correlated well with the difficulty rating assigned by the expert radiologist. Our results suggest that distributed human intelligence is a powerful tool that will aid in the development of CAD for CTC.

Electronic cleansing for CT colonography: does it help CAD software performance in a high-risk population for colorectal cancer?

To compare the performance of computer-aided detection (CAD) for CT colonography (CTC) with and without electronic cleansing (EC) in a high-risk population tagged with a faecal tagging (FT) protocol. Thirty-two patients underwent CTC followed by same-day colonoscopy. All patients underwent bowel preparation and FT with barium and gastrografin. Each CTC dataset was processed with colon CAD with and without EC. Per-polyp sensitivity was calculated. The average number of false-positive (FP) results and their causes were also analysed and compared. Eighty-six polyps were detected in 29 patients. Per-polyp sensitivities of CAD with EC (93.8% and 100%) were higher than those without EC (84.4% and 87.5%) for polyps >or=6 mm and >or=10 mm, respectively. However, the differences were not significant. The average number (6.3) of FPs of CAD with EC was significantly larger than that (3.1) without EC. The distribution of FPs in both CAD settings was also significantly different. The most common cause of FPs was the ileocaecal valve in both datasets. However, untagged faeces was a significantly less common cause of FPs with EC, EC-related artefacts being more common. Electronic cleansing has the potential to improve per-polyp sensitivity of CTC CAD, although the significantly larger number of FPs with EC remains to be improved.

Optimizing computer‐aided colonic polyp detection for CT colonography by evolving the Pareto fronta)

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.

Computer-aided detection for CT colonography: incremental benefit of observer training

The purpose of this study was to investigate the incremental effect of focused training on observer performance when using computer-assisted detection (CAD) software to interpret CT colonography (CTC). Six radiologists who were relatively inexperienced with CTC interpretation underwent 1 day of focused training before reading 20 patient datasets with the assistance of CAD software (ColonCAR 1.3, Medicsight PLC). Sensitivity, specificity and interpretation times were determined and compared with previous performance when reading the same datasets but without the benefit of focused training, using the binomial exact test and Wilcoxon's signed rank test. Per-polyp sensitivity improved after training by 18% overall (95% confidence interval (CI): 14-24%, p<0.001) and was greatest for polyps of 6-9 mm (26%, 95% CI: 18-34%, p<0.001). Absolute sensitivity was 23% (9-36%), 51% (33-71%) and 74% (44-100%) for polyps of <or=5 mm, 6-9 mm and >or=10 mm, respectively. Specificity fell significantly after focused training (median of 5.5 false positives per 20 datasets (interquartile range (IQR): 4-6) post-training vs median of 2.5 (IQR: 1-5) pre-training, p = 0.03). Interpretation time also increased significantly after training (from a median of 9.3 min (IQR: 9.3-14.5 min) to a median of 17.1 min (IQR: 15.4-19.4 min), p = 0.03). In conclusion, one day of training increases observer polyp sensitivity when using CAD for CTC at the expense of increased reporting time and reduction in specificity.

Current Concepts and Future Directions in CT Colonography Computer-Aided Detection

Current Concepts and Future Directions in CT Colonography Computer-Aided Detection