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Simple Summary: Electronic cleansing (EC) is used for performing a virtual cleansing of the colon on CT colonography (CTC) images for colorectal cancer screening. However, current EC methods have limited accuracy, and traditional deep learning is of limited use in CTC. We evaluated the feasibility of using self-supervised adversarial learning to perform EC on a limited dataset with subvoxel accuracy. A 3D generative adversarial network was pre-trained to perform EC on the CTC datasets of an anthropomorphic colon phantom, and it was fine-tuned to each input case by use of a self-supervised learning scheme. The visually perceived quality of the virtual cleansing by this method compared favorably to that of commercial EC software on the virtual 3D fly-through examinations of 18 clinical CTC cases. Our results indicate that the proposed self-supervised scheme is a potentially effective approach for addressing the remaining technical problems of EC in CTC for colorectal cancer screening. Existing electronic cleansing (EC) methods for computed tomographic colonography (CTC) are generally based on image segmentation, which limits their accuracy to that of the underlying voxels. Because of the limitations of the available CTC datasets for training, traditional deep learning is of limited use in EC. The purpose of this study was to evaluate the technical feasibility of using a novel self-supervised adversarial learning scheme to perform EC with a limited training dataset with subvoxel accuracy. A three-dimensional (3D) generative adversarial network (3D GAN) was pre-trained to perform EC on CTC datasets of an anthropomorphic phantom. The 3D GAN was then fine-tuned to each input case by use of the self-supervised scheme. The architecture of the 3D GAN was optimized by use of a phantom study. The visually perceived quality of the virtual cleansing by the resulting 3D GAN compared favorably to that of commercial EC software on the virtual 3D fly-through examinations of 18 clinical CTC cases. Thus, the proposed self-supervised 3D GAN, which can be trained to perform EC on a small dataset without image annotations with subvoxel accuracy, is a potentially effective approach for addressing the remaining technical problems of EC in CTC. [ABSTRACT FROM AUTHOR]

Dual-energy CT provides a promising solution to identify tagged fecal materials in electronic cleansing (EC) for fecal-tagging CT colonography (CTC). In this study, we developed a new EC method based on virtual colon tagging (VCT) for minimizing EC artifacts by use of the material decomposition ability in dual-energy CTC images. In our approach, a localized three-material decomposition model decomposes each voxel into a material mixture vector and the first partial derivatives of three base materials: luminal air, soft tissue, and iodine-tagged fecal material. A Poisson-based derivative smoothing algorithm smoothes the derivatives and implicitly smoothes the associated material mixture fields. VCT is a means for marking the entire colonic lumen by virtually elevating the CT value of luminal air as high as that of the tagged fecal materials to differentiate effectively soft-tissue structures from air-tagging mixtures. A dual-energy EC scheme based on VCT method, denoted as VCT-EC, was developed, in which the colonic lumen was first virtually tagged and then segmented by its high values in VCT images. The performance of the VCT-EC scheme was evaluated in a phantom study and a clinical study. Our results demonstrated that our VCT-EC scheme may provide a significant reduction of EC artifacts. [ABSTRACT FROM PUBLISHER]

We present in this paper a novel dynamic learning method for classifying polyp candidate detections in Computed Tomographic Colonography (CTC) using an adaptation of the Least Square Support Vector Machine (LS-SVM). The proposed technique, called Weighted Proximal Support Vector Machines (WP-SVM), extends the offline capabilities of the SVM scheme to address practical CTC applications. Incremental data are incorporated in the WP-SVM as a weighted vector space, and the only storage requirements are the hyperplane parameters. WP-SVM performance evaluation based on 169 clinical CTC cases using a 3D computer-aided diagnosis (CAD) scheme for feature reduction comparable favorably with previously published CTC CAD studies that have however involved only binary and offline classification schemes. The experimental results obtained from iteratively applying WP-SVM to improve detection sensitivity demonstrate its viability for incremental learning, thereby motivating further follow on research to address a wider range of true positive subclasses such as pedunculated, sessile, and flat polyps, and over a wider range of false positive subclasses such as folds, stool, and tagged materials. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: The purpose of this evaluation was to compare the tagging quality of a barium-based regimen with that of iodine-based regimens for computed tomographic (CT) colonography. Materials and Methods: Tagging quality was assessed retrospectively in three different types of fecal-tagging CT colonographic cases: 24 barium-based cases, 22 nonionic iodine-based cases, and 24 ionic iodine-based cases. For the purpose of evaluation, the large intestine was divided into six segments, and the tagging homogeneity of a total of 420 segments (70 patients) was graded by three blinded readers from 0 (heterogeneous) to 4 (homogeneous). Results: For barium-based cases, the average score for the three readers was 2.4, whereas it was 3.4 for nonionic iodine and 3.6 for ionic iodine. The percentages of segments that were assigned scores of 4 (excellent tagging [100%]) were 11.6%, 61.9%, and 72.9% for the barium-based, nonionic iodine-based, and ionic iodine-based regimens, respectively. The homogeneity scores of iodine-based fecal-tagging regimens were significantly higher than those of the barium-based fecal-tagging regimen (P < .001). The CT attenuation values of tagging in the cases were also assessed: the minimum and maximum values were significantly higher for the iodine-based regimens than for the barium-based regimen (P < .001). Conclusions: The iodine-based fecal-tagging regimens provide significantly greater homogeneity in oral-tagging fecal material than the barium-based fecal-tagging regimen. Iodine-based fecal-tagging regimens can provide an appropriate method for use in nonlaxative or minimum-laxative CT colonography. [Copyright &y& Elsevier]

In CT colonography (CTC), orally administered positive-contrast tagging agents are often used for differentiating residual bowel contents from native colonic structures. However, tagged materials can sometimes hyperattenuate observed CT numbers of their adjacent untagged materials. Such pseudoenhancement complicates the differentiation of colonic soft-tissue structures from tagged materials, because pseudoenhanced colonic structures may have CT numbers that are similar to those of tagged materials. The authors developed a nonlinear regression-based (NLRB) method for performing a local image-based pseudoenhancement correction of CTC data. To calibrate the correction parameters, the CT data of an anthropomorphic reference phantom were correlated with those of partially tagged phantoms. The CTC data were registered spatially by use of an adaptive multiresolution method, and untagged and tagged partial-volume soft-tissue surfaces were correlated by use of a virtual tagging scheme. The NLRB method was then optimized to minimize the difference in the CT numbers of soft-tissue regions between the untagged and tagged phantom CTC data by use of the Nelder-Mead downhill simplex method. To validate the method, the CT numbers of untagged regions were compared with those of registered pseudoenhanced phantom regions before and after the correction. The CT numbers were significantly different before performing the correction (p<0.01), whereas, after the correction, the difference between the CT numbers was not significant. The effect of the correction was also tested on the size measurement of polyps that were covered by tagging in phantoms and in clinical cases. In phantom cases, before the correction, the diameters of 12 simulated polyps submerged in tagged fluids that were measured in a soft-tissue CT display were significantly different from those measured in an untagged phantom (p<0.01), whereas after the correction the difference was not significant. In clinical cases, before the correction, the diameters of 29 colonoscopy-confirmed 3–14 mm polyps affected by tagging that were measured in a soft-tissue CT display were significantly different from those measured in a lung CT display (p<0.0001) or in colonoscopy (p<0.05), whereas after the correction the difference was not significant. Finally, the effect of the correction was tested on automated detection of 25 polyps ≥6 mm affected by tagging in 56 clinical CTC cases. The application of the correction increased the detection accuracy from 60% with 5.0 FP detections per patient without correction to 96% with 2.9 FP detections with correction. This improvement in detection accuracy was statistically significant (p<0.05). The results indicate that the proposed NLRB method can yield an accurate pseudoenhancement correction with potentially significant benefits in clinical CTC examinations. [ABSTRACT FROM AUTHOR]

Laxative-free computed tomographic colonography (lfCTC) could significantly improve patient adherence to colorectal screening. However, the interpretation of lfCTC data is complicated by the presence of poorly tagged feces and partial-volume artifacts that imitate colorectal lesions. The authors developed a method for virtual tagging of such artifacts. A probabilistic model of colonic wall was developed, and virtual tagging was performed on artifacts that were identified by the model. The method was evaluated with 46 clinical lfCTC cases that were prepared with dietary fecal tagging only. Visual examples show that the method can label partial-volume artifacts, poorly tagged feces, nonadhering completely untagged feces, and artifacts such as rectal tubes. The effect of virtual tagging was evaluated by comparing the detection accuracy of a fully automated polyp detection scheme without and with the method. With virtual tagging, the per-lesion detection sensitivity was 100% for lesions >=10 mm (n=4) with 3.8 false positives per patient (per two CT scan volumes) and 90% for lesions >=6 mm (n=10) with 5.4 false positives per patient on average. The improvement in detection performance by virtual tagging was statistically significant (p=0.03; JAFROC and JAFROC-1). [ABSTRACT FROM AUTHOR]

Abstract: In fecal-tagging CT colonography (ftCTC), positive-contrast tagging agents are used for opacifying residual bowel materials to facilitate reliable detection of colorectal lesions. However, tagging agents that have high radiodensity tend to artificially elevate the observed CT attenuation of nearby materials toward that of tagged materials on Hounsfield unit (HU) scale. We developed an image-based adaptive density-correction (ADC) method for minimizing such pseudo-enhancement effect in ftCTC data. After the correction, we can confidently assume that soft-tissue materials and air are represented by their standard CT attenuations, whereas higher CT attenuations indicate tagged materials. The ADC method was optimized by use of an anthropomorphic phantom filled partially with three concentrations of a tagging agent. The effect of ADC on ftCTC was assessed visually and quantitatively by comparison of the accuracy of computer-aided detection (CAD) without and with the use of the ADC method in two different types of clinical ftCTC databases: 20 laxative ftCTC cases with 24 polyps, and 23 reduced-preparation ftCTC cases with 28 polyps. Visual evaluation indicated that ADC minimizes the observed pseudo-enhancement effect. With ADC, the free-response receiver operating characteristic curves indicating CAD performance in polyp detection yielded normalized partial area-under-curve values of 0.91 and 0.80 for the two databases, respectively, with statistically significant improvement over conventional thresholding-based approaches (p <0.05). The results indicate that ADC is a useful method for reducing the pseudo-enhancement effect and for improving CAD performance in CTC. [Copyright &y& Elsevier]

Electronic cleansing (EC) is an emerging method for segmentation of fecal material in CT colonography (CTC) that is used for reducing or eliminating the requirement for cathartic bowel preparation and hence for improving patients’ adherence to recommendations for colon cancer screening. In EC, feces tagged by an x-ray-opaque oral contrast agent are removed from the CTC images, effectively cleansing the colon after image acquisition. Existing EC approaches tend to suffer from the following cleansing artifacts: degradation of soft-tissue structures because of pseudo-enhancement caused by the surrounding tagged fecal materials, and pseudo soft-tissue structures and false fistulas caused by partial volume effects at the boundary between the air lumen and the tagged regions, called the air-tagging boundary (AT boundary). In this study, we developed a novel EC method, called structure-analysis cleansing, which effectively avoids these cleansing artifacts. In our method, submerged soft-tissue structures are recognized by their local morphologic signatures that are characterized based on the eigenvalues of a three-dimensional Hessian matrix. A structure-enhancement function is formulated for enhancing of the soft-tissue structures. In addition, thin folds sandwiched between the air lumen and tagged regions are enhanced by analysis of the local roughness based on multi-scale volumetric curvedness. Both values of the structure-enhancement function and the local roughness are integrated into the speed function of a level set method for delineating the tagged fecal materials. Thus, submerged soft-tissue structures as well as soft-tissue structures adhering to the tagged regions are preserved, whereas the tagged regions are removed along with the associated AT boundaries from CTC images. Evaluation of the quality of the cleansing based on polyps and folds in a colon phantom, as well as on polyps in clinical cathartic and noncathartic CTC cases with fluid and stool tagging, showed that our structure-analysis cleansing method is significantly superior to that of our previous thresholding-based EC method. It provides a cleansed colon with substantially reduced subtraction artifacts. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: Computed tomographic colonography (CTC), also known as virtual colonoscopy, is an emerging alternative technique for screening of colon cancers. CTC uses CT to provide a series of cross-sectional images of the colon for detection of polyps and masses. Fecal tagging is a means of labeling of residual feces by an oral contrast agent for improving the accuracy in the detection of polyps. Computer-aided diagnosis (CAD) for CTC automatically determines the locations of suspicious polyps and masses in CTC and presents them to radiologists, typically as a second opinion. Despite its relatively short history, CAD has become one of the mainstream techniques that could make CTC prime time for screening of colorectal cancer. Rapid technical developments have advanced CAD substantially during the last several years, and a fundamental scheme for the detection of polyps has been established, in which sophisticated 3D image processing, analysis, and display techniques play a pivotal role. The latest CAD systems indicate a clinically acceptable high sensitivity and a low false-positive rate, and observer studies have demonstrated the benefits of these systems in improving radiologists’ detection performance. Some technical and clinical challenges, however, remain unresolved before CAD can become a truly useful tool for clinical practice. Also, new challenges are facing CAD as the methods for bowel preparation and image acquisition, such as tagging of fecal residue with oral contrast agents, and interpretation of CTC images evolve. This article reviews the current status and future challenges in CAD for CTC without and with fecal tagging. [Copyright &y& Elsevier]

Rationale and Objectives: The presence of opacified materials presents several technical challenges for automated detection of polyps in fecal-tagging computed tomography colonography (ftCTC), such as pseudo-enhancement and the distortion of the density, size, and shape of the observed lesions. We developed a fully automated computer-aided detection (CAD) scheme that addresses these issues in automated detection of polyps in ftCTC.Materials and Methods: Pseudo-enhancement was minimized by use of an adaptive density correction (ADC) method. The presence of tagging was minimized by use of an adaptive density mapping (ADM) method. We also developed a new method for automated extraction of the colonic wall within air-filled and tagged regions. The ADC and ADM parameters were optimized by use of an anthropomorphic phantom. The CAD scheme was evaluated with 32+32 cases from two types of clinical ftCTC databases. The cases in database I had full cathartic cleansing and 40 polyps > or =6 mm, and the cases in database II had reduced cathartic cleansing and 44 polyps > or =6 mm. The by-polyp detection performance of the CAD scheme was evaluated by use of a leave-one-patient-out method with five features, and the results were compared with those of a conventional CAD scheme by use of free-response receiver operating characteristic curves.Results: The CAD scheme detected 95% and 86% of the polyps > or =6 mm with 3.6 and 4.2 false positives per scan on average in databases I and II, respectively. For polyps > or =10 mm, the detection sensitivity was 94% in database I (with one missed hyperplastic polyp) and 100% in database II at the same false-positive rate. The detection sensitivity of the new CAD scheme was approximately 20% higher than that of the conventional CAD scheme.Conclusions: The results show that the CAD scheme developed in this study resolves the technical challenges introduced by fecal tagging, is applicable to a variety of colon preparation regimens, and provides a performance superior to that of conventional CAD schemes. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: Radiologists often compare the supine and prone data sets of a patient to confirm potential polyp findings in computed tomographic (CT) colonography (CTC). We developed a new automated method that uses region-based supine-prone correspondence for the reduction of false-positive (FP) polyp candidates in computer-aided detection (CAD) for CTC. Materials and Methods: Up to six anatomic landmarks are established by use of the extracted region of the colonic lumen. A region-growing scheme with distance calculations is used to divide the colonic lumen into overlapping segments that match in the supine and prone data sets. Polyp candidates detected by means of a CAD scheme are eliminated in colonic segments that have sufficient diagnostic quality and contain polyp candidates in only one of the data sets of a patient. The method was evaluated with 121 CTC cases, including 42 polyps of 5 mm or greater in 28 patients, obtained by use of single- and multidetector CT scanners with standard pre-colonoscopy cleansing. Results: Complete or partial correspondence was established in 71% of cases. Based on a leave-one-patient-out evaluation, application of the method reduced 19% of FP results reported by our CAD scheme at a 90.5% by-polyp detection sensitivity, without loss of any true-positive results. The resulting CAD scheme yielded 2.4 FP results per patient, on average, with the use of the correspondence method, whereas it yielded 3.0 FP results per patient without the use of the method. Conclusion: The correspondence method is potentially useful for improving the specificity of CAD in CTC. [ABSTRACT FROM AUTHOR]

We developed a new Visualization method for virtual endoscopic examination of computed tomographic (CT) colonographic data by use of shape-scale analysis. The method provides each colonic structure of interest with a unique color, thereby facilitating rapid diagnosis of the colon. Two shape features, called the local shape index and curvedness, are used for defining the shape-scale spectrum. When we map the shape index and curvedness values within CT colonographic data to the shape-scale spectrum, specific types of colonic structures are represented by unique characteristic signatures in the spectrum. The characteristic signatures of specific types of lesions can be determined by use of computer-simulated lesions or by use of clinical data sets subjected to a computerized detection scheme. The signatures are used for defining a two-dimensional color map by assignment of a unique color to each signature region. The method was evaluated visually by use of computer-simulated lesions and clinical CT colonographic data sets, as well as by an evaluation of the human observer performance in the detection of polyps without and with the use of the color maps. The results indicate that the coloring of the colon yielded by the shape-scale color maps can be used for differentiating among the chosen colonic structures. Moreover, the results indicate that the use of the shape-scale color maps can improve the performance of radiologists in the detection of polyps in CT colonography. [ABSTRACT FROM AUTHOR]

Heterogeneous Data Analysis (HDA) is proposed to address a learning problem of medical image databases of Computed Tomographic Colonography (CTC). The databases are generated from clinical CTC images using a Computer-aided Detection (CAD) system, the goal of which is to aid radiologists' interpretation of CTC images by providing highly accurate, machine-based detection of colonic polyps. We aim to achieve a high detection accuracy in CAD in a clinically realistic context, in which additional CTC cases of new patients are added regularly to an existing database. In this context, the CAD performance can be improved by exploiting the heterogeneity information that is brought into the database through the addition of diverse and disparate patient populations. In the HDA, several quantitative criteria of data compatibility are proposed for efficient management of these online images. After an initial supervised offline learning phase, the proposed online learning method decides whether the online data are heterogeneous or homogeneous. Our previously developed Principal Composite Kernel Feature Analysis (PC-KFA) is applied to the online data, managed with HDA, for iterative construction of a linear subspace of a high-dimensional feature space by maximizing the variance of the non-linearly transformed samples. The experimental results showed that significant improvements in the data compatibility were obtained when the online PC-KFA was used, based on an accuracy measure for long-term sequential online datasets. The computational time is reduced by more than 93% in online training compared with that of offline training. [ABSTRACT FROM AUTHOR]