Detection Of Polyps In CT Colonography Research Articles

Improved Sensitivity and Reader Confidence in CT Colonography Using Dual-Layer Spectral CT: A Phantom Study.

Background Limited cathartic preparations for CT colonography with fecal tagging can improve patient comfort but may result in nondiagnostic examinations from poorly tagged stool. Dual-energy CT may overcome this limitation by improving the conspicuity of the contrast agent, but more data are needed. Purpose To investigate whether dual-energy CT improves polyp detection in CT colonography compared with conventional CT at different fecal tagging levels in vitro. Materials and Methods In this HIPAA-compliant study, between December 2017 and August 2019, a colon phantom 30 cm in diameter containing 60 polyps of different shapes (spherical, ellipsoid, flat) and size groups (5-9 mm, 11-15 mm) was constructed and serially filled with simulated feces tagged with four different iodine concentrations (1.26, 2.45, 4.88, and 21.00 mg of iodine per milliliter), then it was scanned with dual-energy CT with and without an outer fat ring to simulate large body size (total diameter, 42 cm). Two readers independently reviewed conventional 120-kVp CT and 40-keV monoenergetic dual-energy CT images to record the presence of polyps and confidence (three-point scale.) Generalized estimating equations were used for sensitivity comparisons between conventional CT and dual-energy CT, and a Wilcoxon signed-rank test was used for reader confidence. Results Dual-energy CT had higher overall sensitivity for polyp detection than conventional CT (58.8%; 95% confidence interval [CI]: 49.7%, 67.3%; 564 of 960 polyps vs 42.1%; 95% CI: 32.1%, 52.8%; 404 of 960 polyps; P < .001), including with the fat ring (48% and 31%, P < .001). Reader confidence improved with dual-energy CT compared with conventional images on all tagging levels (P < .001). Interrater agreement was substantial (κ = 0.74; 95% CI: 0.70, 0.77). Conclusion Compared with conventional 120-kVp CT, dual-energy CT improved polyp detection and reader confidence in a dedicated dual-energy CT colonography phantom, especially with suboptimal fecal tagging. © RSNA, 2020.

Adaptive kernel based multiple kernel learning for computer-aided polyp detection in CT colonography

Adaptive kernel based multiple kernel learning for computer-aided polyp detection in CT colonography

ROC operating point selection for classification of imbalanced data with application to computer-aided polyp detection in CT colonography

Computer-aided detection and diagnosis (CAD) of colonic polyps always faces the challenge of classifying imbalanced data. In this paper, three new operating point selection strategies based on receiver operating characteristic curve are proposed to address the problem. Classification on imbalanced data performs inferiorly because of a major reason that the best differentiation threshold shifts due to the degree of data imbalance. To address this decision threshold shifting issue, three operating point selection strategies, i.e., shortest distance, harmonic mean and anti-harmonic mean, are proposed and their performances are investigated. Experiments were conducted on a class-imbalanced database, which contains 64 polyps in 786 polyp candidates. Support vector machine (SVM) and random forests (RFs) were employed as basic classifiers. Two imbalanced data correcting techniques, i.e., cost-sensitive learning and training data down sampling, were applied to SVM and RFs, and their performances were compared with the proposed strategies. Comparing to the original thresholding method, i.e., 0.488 sensitivity and 0.986 specificity for RFs and 0.526 sensitivity and 0.977 specificity for SVM, our strategies achieved more balanced results, which are around 0.89 sensitivity and 0.92 specificity for RFs and 0.88 sensitivity and 0.90 specificity for SVM. Meanwhile, their performance remained at the same level regardless of whether other correcting methods are used. Based on the above experiments, the gain of our proposed strategies is noticeable: the sensitivity improved from 0.5 to around 0.88 for RFs and 0.89 for SVM while remaining a relatively high level of specificity, i.e., 0.92 for RFs and 0.90 for SVM. The performance of our proposed strategies was adaptive and robust with different levels of imbalanced data. This indicates a feasible solution to the shifting problem for favorable sensitivity and specificity in CAD of polyps from imbalanced data.

Improved computer‐aided detection of small polyps in CT colonography using interpolation for curvature estimationa)

Surface curvatures are important geometric features for the computer-aided analysis and detection of polyps in CT colonography (CTC). However, the general kernel approach for curvature computation can yield erroneous results for small polyps and for polyps that lie on haustral folds. Those erroneous curvatures will reduce the performance of polyp detection. This paper presents an analysis of interpolation's effect on curvature estimation for thin structures and its application on computer-aided detection of small polyps in CTC. The authors demonstrated that a simple technique, image interpolation, can improve the accuracy of curvature estimation for thin structures and thus significantly improve the sensitivity of small polyp detection in CTC. Our experiments showed that the merits of interpolating included more accurate curvature values for simulated data, and isolation of polyps near folds for clinical data. After testing on a large clinical data set, it was observed that sensitivities with linear, quadratic B-spline and cubic B-spline interpolations significantly improved the sensitivity for small polyp detection. The image interpolation can improve the accuracy of curvature estimation for thin structures and thus improve the computer-aided detection of small polyps in CTC.

From Point to Local Neighborhood: Polyp Detection in CT Colonography Using Geodesic Ring Neighborhoods

Existing polyp detection methods rely heavily on curvature-based characteristics to differentiate between lesions. These assume that the discrete triangulated surface mesh or volume closely approximates a smooth continuous surface. However, this is often not the case and because curvature is computed as a local feature and a second-order differential quantity, the presence of noise significantly affects its estimation. For this reason, a more global feature is required to provide an accurate description of the surface at hand. In this paper, a novel method incorporating a local neighborhood around the centroid of a surface patch is proposed. This is done using geodesic rings which accumulate curvature information in a neighborhood around this centroid. This geodesic-ring neighborhood approximates a single smooth, continuous surface upon which curvature and orientation estimation methods can be applied. A new global shape index, S is also introduced and computed. These curvature and orientation values will be used to classify the surface as either a bulbous polyp, ridge-like fold or semiplanar structure. Experimental results show that this method is promising (100% sensitivity, 100% specificity for lesions > 10 mm) for distinguishing between bulbous polyps, folds and planar-like structures in the colon.

Scale‐based scatter correction for computer‐aided polyp detection in CT colonography

CT colonography (CTC) is a feasible and minimally invasive method for the detection of colorectal polyps and cancer screening. Computer-aided detection (CAD) of polyps can improve consistency and sensitivity of virtual colonoscopy interpretation and reduce interpretation burden. However, high-density orally administered contrast agents have scatter effects on neighboring tissues. The scattering manifests itself as an artificial elevation in the observed CT attenuation values of the neighboring tissues. This pseudoenhancement phenomenon presents a problem for the application of computer-aided polyp detection, especially when polyps are submerged in the contrast agents. The authors have developed a scale-based correction method that minimizes scatter effects in CTC data by subtraction of the estimated scatter components from observed CT attenuations. By bringing a locally adaptive structure, object scale, into the correction framework, the region of neighboring tissues affected by contrast agents is automatically specified and adaptively changed in different parts of the image. The method was developed as one preprocessing step in the authors' CAD system and was tested by using leave-one-patient-out evaluation on 56 clinical CTC scans (supine or prone) from 28 patients. There were 50 colonoscopy-confirmed polyps measuring 6-9 mm. Visual evaluation indicated that the method reduced CT attenuation of pseudoenhanced polyps to the usual polyp Hounsfield unit range without affecting luminal air regions. For polyps submerged in contrast agents, the sensitivity of CAD with correction is increased 24% at a rate of ten false-positive detections per scan. For all polyps within 6-9 mm, the sensitivity of the authors' CAD with scatter correction is increased 8% at a rate of ten false-positive detections per scan. The authors' results indicated that CAD with this correction method as a preprocessing step can yield a high sensitivity and a relatively low FP rate in CTC.

Mixture of expert 3D massive-training ANNs for reduction of multiple types of false positives in CAD for detection of polyps in CT colonography

One of the major challenges in computer-aided detection (CAD) of polyps in CT colonography (CTC) is the reduction of false-positive detections (FPs) without a concomitant reduction in sensitivity. A large number of FPs is likely to confound the radiologist's task of image interpretation, lower the radiologist's efficiency, and cause radiologists to lose their confidence in CAD as a useful tool. Major sources of FPs generated by CAD schemes include haustral folds, residual stool, rectal tubes, the ileocecal valve, and extra-colonic structures such as the small bowel and stomach. Our purpose in this study was to develop a method for the removal of various types of FPs in CAD of polyps while maintaining a high sensitivity. To achieve this, we developed a "mixture of expert" three-dimensional (3D) massive-training artificial neural networks (MTANNs) consisting of four 3D MTANNs that were designed to differentiate between polyps and four categories of FPs: (1) rectal tubes, (2) stool with bubbles, (3) colonic walls with haustral folds, and (4) solid stool. Each expert 3D MTANN was trained with examples from a specific non-polyp category along with typical polyps. The four expert 3D MTANNs were combined with a mixing artificial neural network (ANN) such that different types of FPs could be removed. Our database consisted of 146 CTC datasets obtained from 73 patients whose colons were prepared by standard pre-colonoscopy cleansing. Each patient was scanned in both supine and prone positions. Radiologists established the locations of polyps through the use of optical-colonoscopy reports. Fifteen patients had 28 polyps, 15 of which were 5-9 mm and 13 were 10-25 mm in size. The CTC cases were subjected to our previously reported CAD method consisting of centerline-based extraction of the colon, shape-based detection of polyp candidates, and a Bayesian-ANN-based classification of polyps. The original CAD method yielded 96.4% (27/28) by-polyp sensitivity with an average of 3.1 (224/73) FPs per patient. The mixture of expert 3D MTANNs removed 63% (142/224) of the FPs without the loss of any true positive; thus, the FP rate of our CAD scheme was improved to 1.1 (82/73) FPs per patient while the original sensitivity was maintained. By use of the mixture of expert 3D MTANNs, the specificity of a CAD scheme for detection of polyps in CTC was substantially improved while a high sensitivity was maintained.

HDF: Heat diffusion fields for polyp detection in CT colonography

Computer aided detection (CAD) in computed tomography colonography (CTC) aims at detecting colonic polyps that are the precursors of cancer. We propose to use heat diffusion patterns for polyp detection and identification in CTC. The underlying idea of the proposed method is to utilize the heat diffusion process, which is closely related to the diffusion filtering, to generate a vector field (HDF) that is correlated with the shape of the colon wall. The HDF and a local voxel distribution parameter were used for detection and identification. We evaluated the method on real patient CTC data acquired from eight volunteers. 100% sensitivity is achieved at 0.53 false positives per patient for polyps ⩾ 8 mm and 78% sensitivity is achieved at 0.53 false positives per patient when all polyps ⩾ 5 mm are considered.

Support vector machines committee classification method for computer-aided polyp detection in CT colonography 1

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.

Evaluation of the effect of CAD on observers' performance in detection of polyps in CT colonography

We conducted an observer study to evaluate the effect of computer-aided detection (CAD) on human readers' performance in the detection of polyps in CT colonography (CTC). Twenty data sets including 11 polyps were retrospectively selected from our CTC database. A sequential test was used, in which four observers interpreted CTC examinations by use of our colon CAD workstation in two sequential sessions: one without CAD and the other with CAD. At each session, the observers rated the confidence level regarding whether the case was abnormal, i.e., at least one polyp>5 mm was present in the colon. Receiver operating characteristic (ROC) analysis was performed based on the confidence levels for the abnormality of the CTC data sets, and the area under the ROC curve (Az) was calculated as a measure of the observers' detection performance. The results showed that, for all of the observers, the detection performance increased with use of CAD. The average Az values without and with CAD were 0.70 and 0.85, respectively. A two-tailed t-test showed that the difference between these Az values was statistically significant ( p=0.025). The result indicates that CAD can be a useful tool for improving human readers' performance in the detection of polyps in CTC.

Computer-aided polyp detection in CT colonography using an ensemble of support vector machines

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.