-
[show abstract]
[hide abstract]
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.
Medical Physics 08/2008; 35(8):3527-38. · 2.83 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: 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.
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.
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.
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.
American Journal of Roentgenology 08/2008; 191(1):168-74. · 2.78 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: 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.
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.
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.
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.
American Journal of Roentgenology 01/2008; 189(6):1457-63. · 2.78 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: This HIPAA-compliant study, with institutional review board approval and informed patient consent, was conducted to retrospectively develop a teniae coli-based circumferential localization method for guiding virtual colon navigation and colonic polyp registration. Colonic surfaces (n = 72) were depicted at computed tomographic (CT) colonography performed in 36 patients (26 men, 10 women; age range, 47-72 years) in the supine and prone positions. For 70 (97%) colonic surfaces, the tenia omentalis (TO), the most visible of the three teniae coli on a well-distended colonic surface, was manually extracted from the cecum to the descending colon. By virtually dissecting and flattening the colon along the TO, the authors developed a localization system involving 12 grid lines to estimate the circumferential positions of polyps. A sessile polyp would most likely (at 95% confidence level) be found within +/-1.2 grid lines (one grid line equals 1/12 the circumference) with use of the proposed method. By orienting and positioning the virtual cameras with use of the new localization system, synchronized prone and supine navigation was achieved. The teniae coli are extractable landmarks, and the teniae coli-based circumferential localization system helps guide virtual navigation and polyp registration at CT colonography.
Radiology 06/2007; 243(2):551-60. · 5.73 Impact Factor
-
Ronald M Summers,
Adam Huang,
Jianhua Yao,
Shannon R Campbell,
Jennifer E Dempsey,
Andrew J Dwyer, Marek Franaszek,
Danny S Brickman,
Ingmar Bitter,
Nicholas Petrick,
Amy K Hara
[show abstract]
[hide abstract]
ABSTRACT: We sought to demonstrate that intravenous contrast-enhanced CT colonography (CTC) can distinguish colonic adenomas from carcinomas.
Supine intravenous contrast-enhanced CTC with colonoscopic and/or surgical correlation was performed on 25 patients with colonic adenomas or carcinomas. Standard deviation of mean polyp CT attenuation was computed and assessed using ANOVA and receiver-operating characteristic analyses.
Colonoscopy confirmed 32 polyps or masses 1 to 8 cm in size. The standard deviations of CT attenuation were carcinomas (n = 13; 36 +/- 6 HU; range 28-48 HU) and adenomas (n = 19; 49 +/- 14 HU; range 31-100 HU) (P = 0.005). At a standard deviation threshold of 42 HU, the sensitivity and specificity for classifying a polyp or mass as a carcinoma were 92% and 79%, respectively. The area under the receiver-operating characteristic curve was 0.89 +/- 0.06 (95% confidence interval 0.73-0.96).
Measurement of the standard deviation of CT attenuation on intravenous contrast-enhanced CTC permits histopathologic classification of polyps 1 cm or larger as carcinomas versus adenomas. The presence of ulceration or absence of muscular invasion in carcinomas creates overlap with adenomas, reducing the specificity of carcinoma classification.
Academic Radiology 01/2007; 13(12):1490-5. · 1.69 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Reliable segmentation of the colon is a requirement for three-dimensional visualization programs and automatic detection of polyps on computed tomography (CT) colonography. There is an evolving clinical consensus that giving patients positive oral contrast to tag out remnants of stool and residual fluids is mandatory. The presence of positive oral contrast in the colon adds an additional challenge for colonic segmentation but ultimately is beneficial to the patient because the enhanced fluid helps reveal polyps in otherwise hidden areas. Therefore, we developed a new segmentation procedure which can handle both air- and fluid-filled parts of the colon. The procedure organizes individual air- and fluid-filled regions into a graph that enables identification and removal of undesired leakage outside the colon. In addition, the procedure provides a risk assessment of possible leakage to assist the user prior to the tedious task of visual verification. The proposed hybrid algorithm uses modified region growing, fuzzy connectedness and level set segmentation. We tested our algorithm on 160 CT colonography scans containing 183 known polyps. All 183 polyps were in segmented regions. In addition, visual inspection of 24 CT colonography scans demonstrated good performance of our procedure: the reconstructed colonic wall appeared smooth even at the interface between air and fluid and there were no leaked regions.
IEEE Transactions on Medical Imaging 04/2006; 25(3):358-68. · 3.64 Impact Factor
-
Proceedings of the 2006 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Arlington, VA, USA, 6-9 April 2006; 01/2006
-
[show abstract]
[hide abstract]
ABSTRACT: The sensitivity of computed tomographic (CT) virtual colonoscopy (CT colonography) for detecting polyps varies widely in recently reported large clinical trials. Our objective was to determine whether a computer program is as sensitive as optical colonoscopy for the detection of adenomatous colonic polyps on CT virtual colonoscopy.
The data set was a cohort of 1186 screening patients at 3 medical centers. All patients underwent same-day virtual and optical colonoscopy. Our enhanced gold standard combined segmental unblinded optical colonoscopy and retrospective identification of precise polyp locations. The data were randomized into separate training (n = 394) and test (n = 792) sets for analysis by a computer-aided polyp detection (CAD) program.
For the test set, per-polyp and per-patient sensitivities for CAD were both 89.3% (25/28; 95% confidence interval, 71.8%-97.7%) for detecting retrospectively identifiable adenomatous polyps at least 1 cm in size. The false-positive rate was 2.1 (95% confidence interval, 2.0-2.2) false polyps per patient. Both carcinomas were detected by CAD at a false-positive rate of 0.7 per patient; only 1 of 2 was detected by optical colonoscopy before segmental unblinding. At both 8-mm and 10-mm adenoma size thresholds, the per-patient sensitivities of CAD were not significantly different from those of optical colonoscopy before segmental unblinding.
The per-patient sensitivity of CT virtual colonoscopy CAD in an asymptomatic screening population is comparable to that of optical colonoscopy for adenomas > or = 8 mm and is generalizable to new CT virtual colonoscopy data.
Gastroenterology 12/2005; 129(6):1832-44. · 11.68 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: A new classification scheme for the computer-aided detection of colonic polyps in computed tomographic colonography is proposed.
The scheme involves an ensemble of support vector machines (SVMs) for classification, a smoothed leave-one-out (SLOO) cross-validation method for obtaining error estimates, and use of a bootstrap aggregation method for training and model selection. Our use of an ensemble of SVM classifiers with bagging (bootstrap aggregation), built on different feature subsets, is intended to improve classification performance compared with single SVMs and reduce the number of false-positive detections. The bootstrap-based model-selection technique is used for tuning SVM parameters. In our first experiment, two independent data sets were used: the first, for feature and model selection, and the second, for testing to evaluate the generalizability of our model. In the second experiment, the test set that contained higher resolution data was used for training and testing (using the SLOO method) to compare SVM committee and single SVM performance.
The overall sensitivity on independent test set was 75%, with 1.5 false-positive detections/study, compared with 76%-78% sensitivity and 4.5 false-positive detections/study estimated using the SLOO method on the training set. The sensitivity of the SVM ensemble retrained on the former test set estimated using the SLOO method was 81%, which is 7%-10% greater than the sensitivity of a single SVM. The number of false-positive detections per study was 2.6, a 1.5 times reduction compared with a single SVM.
Training an SVM ensemble on one data set and testing it on the independent data has shown that the SVM committee classification method has good generalizability and achieves high sensitivity and a low false-positive rate. The model selection and improved error estimation method are effective for computer-aided polyp detection.
Academic Radiology 05/2005; 12(4):479-86. · 1.69 Impact Factor
-
American Journal of Roentgenology 02/2005; 184(1):105-8. · 2.78 Impact Factor
-
16th IEEE Visualization Conference (VIS 2005), 23-28 October 2005, Minneapolis, MN, USA; 01/2005
-
[show abstract]
[hide abstract]
ABSTRACT: An automatic method to segment colonic polyps in computed tomography (CT) colonography is presented in this paper. The method is based on a combination of knowledge-guided intensity adjustment, fuzzy c-mean clustering, and deformable models. The computer segmentations were compared with manual segmentations to validate the accuracy of our method. An average 76.3% volume overlap percentage among 105 polyp detections was reported in the validation, which was very good considering the small polyp size. Several experiments were performed to investigate the intraoperator and interoperator repeatability of manual colonic polyp segmentation. The investigation demonstrated that the computer-human repeatability was as good as the interoperator repeatability. The polyp segmentation was also applied in computer-aided detection (CAD) to reduce the number of false positive (FP) detections and provide volumetric features for polyp classification. Our segmentation method was able to eliminate 30% of FP detections. The volumetric features computed from the segmentation can further reduce FP detections by 50% at 80% sensitivity.
IEEE Transactions on Medical Imaging 12/2004; 23(11):1344-52. · 3.64 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: A new classification system for colonic polyp detection, designed to increase sensitivity and reduce the number of false-positive findings with computed tomographic colonography, was developed and tested in this study.
The system involves classification by a committee of neural networks (NNs), each using largely distinct subsets of features selected from a general set. Back-propagation NNs trained with the Levenberg-Marquardt algorithm were used as primary classifiers (committee members). The set of features included region density, Gaussian and mean curvature and sphericity, lesion size, colon wall thickness, and the means and standard deviations of all of these values. Subsets of variables were initially selected because of their effectiveness according to training and test sample misclassification rates. The final decision for each case is based on the majority vote across the networks and reflects the weighted votes of all networks. The authors also introduce a smoothed cross-validation method designed to improve estimation of the true misclassification rates by reducing bias and variance.
This committee method reduced the false-positive rate by 36%, a clinically meaningful reduction, and improved sensitivity by an average of 6.9% compared with decisions made by any single NN. The overall sensitivity and specificity were 82.9% and 95.3%, respectively, when sensitivity was estimated by means of smoothed cross-validation.
The proposed method of using multiple classifiers and majority voting is recommended for classification tasks with large sets of input features, particularly when selected feature subsets may not be equally effective and do not provide satisfactory true- and false-positive rates. This approach reduces variance in estimates of misclassification rates.
Academic Radiology 03/2003; 10(2):154-60. · 1.69 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Detection of colonic polyps in CT colonography is problematic due to complexities of polyp shape and the surface of the normal colon. Published results indicate the feasibility of computer-aided detection of polyps but better classifiers are needed to improve specificity. In this paper we compare the classification results of two approaches: neural networks and recursive binary trees. As our starting point we collect surface geometry information from three-dimensional reconstruction of the colon, followed by a filter based on selected variables such as region density, Gaussian and average curvature and sphericity. The filter returns sites that are candidate polyps, based on earlier work using detection thresholds, to which the neural nets or the binary trees are applied. A data set of 39 polyps from 3 to 25 mm in size was used in our investigation. For both neural net and binary trees we use tenfold cross-validation to better estimate the true error rates. The backpropagation neural net with one hidden layer trained with Levenberg-Marquardt algorithm achieved the best results: sensitivity 90% and specificity 95% with 16 false positives per study.
Medical Physics 02/2003; 30(1):52-60. · 2.83 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: To apply a computer-aided detection (CAD) algorithm to supine and prone multisection helical computed tomographic (CT) colonographic images to confirm if there is any added benefit provided by CAD over that of standard clinical interpretation.
CT colonography (with patients in both supine and prone positions) was performed with a multisection helical CT scanner in 40 asymptomatic high-risk patients. There were two consecutive series of patients, 20 of whom had at least one polyp 1.0 cm in size or larger and 20 of whom had normal colons at conventional colonoscopy performed the same day. The CT colonographic images were interpreted with an automated CAD algorithm and by two radiologists who were blinded to colonoscopy findings.
For 25 polyps at least 1.0 cm in size ("large" polyps), sensitivity for detection by at least one radiologist was 48% (12 of 25). The sensitivity of CAD for detecting large polyps was also 48% (12 of 25), but the CAD algorithm detected four of 13 large polyps that were not detected by either radiologist (31%, 95% two-sided CI: 9, 61), increasing the potential sensitivity to 64% (16 of 25). For polyps identifiable retrospectively, sensitivity of CAD was 67% (12 of 18), and sensitivity of the combination of detection with the CAD algorithm or by at least one radiologist was 89% (16 of 18). There were an average of 11 false-positive detections per patient for CAD.
In this series of patients in whom radiologists had difficulties detecting polyps (compared with sensitivities of 75%-90% reported in the literature), this CAD algorithm played a complementary role to conventional interpretation of CT colonographic images by detecting a number of large polyps missed by trained observers.
Radiology 12/2002; 225(2):391-9. · 5.73 Impact Factor
-
Proceedings of the 2002 IEEE International Symposium on Biomedical Imaging, Ritz-Carlton Hotel, Washington, DC, USA, 7-10 June 2002; 01/2002
-
[show abstract]
[hide abstract]
ABSTRACT: A high-throughput, integrated software system to investigate computer-aided diagnosis for CT colonography is described and demonstrated. The system allows investigation of numerous parameters and algorithms critical to the goal of automated localization of possible polyps. The system forms the foundation of our future research efforts.
International Congress Series.
-
[show abstract]
[hide abstract]
ABSTRACT: In this paper, we propose a new classification scheme for computer-aided detection (CAD) of colonic polyps in CT colonography (CTC). The scheme involves an ensemble of support vector machines (SVMs) for classification, a smoothed leave-one-out (SLOO) cross-validation method for obtaining error estimates, and the use of a bootstrap aggregation method for training and model selection. Our use of an ensemble of SVM classifiers with bagging (bootstrap aggregation), built on different feature subsets, is intended to improve classification performance when compared to single SVMs and to reduce the number of false positive detections. The bagging technique has the effect of a virtual increase in the training set size and, as a consequence, also helps to reduce the bias of error estimates when combined with a leave-one-out cross-validation approach. The bootstrap-based model selection technique is used for tuning the SVM parameters.
International Congress Series.
