Adaptive correction of the pseudo-enhancement of CT attenuation for fecal-tagging CT colonography

Self-Supervised Adversarial Learning with a Limited Dataset for Electronic Cleansing in Computed Tomographic Colonography: A Preliminary Feasibility Study

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.

Automatic Detection and Segmentation of Colorectal Cancer with Deep Residual Convolutional Neural Network

Early and automatic detection of colorectal tumors is essential for cancer analysis, and the same is implemented using computer-aided diagnosis (CAD). A computerized tomography (CT) image of the colon is being used to identify colorectal carcinoma. Digital imaging and communication in medicine (DICOM) is a standard medical imaging format to process and analyze images digitally. Accurate detection of tumor cells in the complex digestive tract is necessary for optimal treatment. The proposed work is divided into two phases. The first phase involves the segmentation, and the second phase is the extraction of the colon lesions with the observed segmentation parameters. A deep convolutional neural network (DCNN) based residual network approach for the colon and polyps' segmentation from the CT images is applied over the 2D CT images. The residual stack block is being added to the hidden layers with short skip nuance, which helps to retain spatial information. ResNet-enabled CNN is employed in the current work to achieve complete boundary segmentation of the colon cancer region. The results obtained through segmentation serve as features for further extraction and classification of benign as well as malignant colon cancer. Performance evaluation metrics indicate that the proposed network model has effectively segmented and classified colorectal tumors with dice scores of 91.57% (on average), sensitivity = 98.28, specificity = 98.68, and accuracy = 98.82.

A generative flow-based model for volumetric data augmentation in 3D deep learning for computed tomographic colonography

Purpose

Deep learning can be used for improving the performance of computer-aided detection (CADe) in various medical imaging tasks. However, in computed tomographic (CT) colonography, the performance is limited by the relatively small size and the variety of the available training datasets. Our purpose in this study was to develop and evaluate a flow-based generative model for performing 3D data augmentation of colorectal polyps for effective training of deep learning in CADe for CT colonography.

Methods

We developed a 3D-convolutional neural network (3D CNN) based on a flow-based generative model (3D Glow) for generating synthetic volumes of interest (VOIs) that has characteristics similar to those of the VOIs of its training dataset. The 3D Glow was trained to generate synthetic VOIs of polyps by use of our clinical CT colonography case collection. The evaluation was performed by use of a human observer study with three observers and by use of a CADe-based polyp classification study with a 3D DenseNet.

Results

The area-under-the-curve values of the receiver operating characteristic analysis of the three observers were not statistically significantly different in distinguishing between real polyps and synthetic polyps. When trained with data augmentation by 3D Glow, the 3D DenseNet yielded a statistically significantly higher polyp classification performance than when it was trained with alternative augmentation methods.

Conclusion

The 3D Glow-generated synthetic polyps are visually indistinguishable from real colorectal polyps. Their application to data augmentation can substantially improve the performance of 3D CNNs in CADe for CT colonography. Thus, 3D Glow is a promising method for improving the performance of deep learning in CADe for CT colonography.

A scalable approach to T2-MRI colon segmentation

The study of the colonic volume is a procedure with strong relevance to gastroenterologists. Depending on the clinical protocols, the volume analysis has to be performed on MRI of the unprepared colon without contrast administration. In such circumstances, existing measurement procedures are cumbersome and time-consuming for the specialists. The algorithm presented in this paper permits a quasi-automatic segmentation of the unprepared colon on T2-weighted MRI scans. The segmentation algorithm is organized as a three-stage pipeline. In the first stage, a custom tubularity filter is run to detect colon candidate areas. The specialists provide a list of points along the colon trajectory, which are combined with tubularity information to calculate an estimation of the colon medial path. In the second stage, we delimit the region of interest by applying custom segmentation algorithms to detect colon neighboring regions and the fat capsule containing abdominal organs. Finally, within the reduced search space, segmentation is performed via 3D graph-cuts in a three-stage multigrid approach. Our algorithm was tested on MRI abdominal scans, including different acquisition resolutions, and its results were compared to the colon ground truth segmentations provided by the specialists. The experiments proved the accuracy, efficiency, and usability of the algorithm, while the variability of the scan resolutions contributed to demonstrate the computational scalability of the multigrid architecture. The system is fully applicable to the colon measurement clinical routine, being a substantial step towards a fully automated segmentation.

Dual-energy computed tomography colonography using dual-layer spectral detector computed tomography: Utility of virtual monochromatic imaging for electronic cleansing

OBJECTIVE

To assess the utility of virtual monochromatic imaging (VMI) using a dual-layer spectral detector CT for electronic cleansing in fecal-tagging CT colonography (CTC).

METHODS

This study included 35 patients who underwent fecal-tagging CTC with a dual-layer detector spectral CT scanner. Conventional images at 120 kVp and VMI at 40, 50, and 60 keV were reconstructed. Quantitative image quality parameters, i.e., tagging density and image noise, were compared and the visual image quality was scored on a four-point scale. We recorded the number of the colon segments with appropriate tagging density (≥300 HU) for each patient and used these data to compare the reconstructions. In addition, electronic cleansing performance was semi-quantitatively assessed using a four-point scale.

RESULTS

The mean tagging density on VMI was significantly higher than that on conventional 120 kVp images. The number of colon segments with appropriate tagging density on VMI were significantly higher than that on conventional 120 kVp images. There was no significant difference among the reconstructed images with respect to image noise. Scores for subjective image quality and electronic cleansing performance on VMI were significantly higher than those on conventional 120 kVp images.

CONCLUSION

With dual-layer spectral detector CT, VMI can yield significantly better fecal-tagged CTC image quality and improve electronic cleansing performance.

Deep Learning Electronic Cleansing for Single- and Dual-Energy CT Colonography

Electronic cleansing (EC) is used for computational removal of residual feces and fluid tagged with an orally administered contrast agent on CT colonographic images to improve the visibility of polyps during virtual endoscopic "fly-through" reading. A recent trend in CT colonography is to perform a low-dose CT scanning protocol with the patient having undergone reduced- or noncathartic bowel preparation. Although several EC schemes exist, they have been developed for use with cathartic bowel preparation and high-radiation-dose CT, and thus, at a low dose with noncathartic bowel preparation, they tend to generate cleansing artifacts that distract and mislead readers. Deep learning can be used for improvement of the image quality with EC at CT colonography. Deep learning EC can produce substantially fewer cleansing artifacts at dual-energy than at single-energy CT colonography, because the dual-energy information can be used to identify relevant material in the colon more precisely than is possible with the single x-ray attenuation value. Because the number of annotated training images is limited at CT colonography, transfer learning can be used for appropriate training of deep learning algorithms. The purposes of this article are to review the causes of cleansing artifacts that distract and mislead readers in conventional EC schemes, to describe the applications of deep learning and dual-energy CT colonography to EC of the colon, and to demonstrate the improvements in image quality with EC and deep learning at single-energy and dual-energy CT colonography with noncathartic bowel preparation. ©RSNA, 2018.

Electronic cleansing in computed tomography colonography using AT layer identification with integration of gradient directional second derivative and material fraction model

Background

In computed tomography colonography images, electronic cleansing (EC) is applied to remove opacified residual materials, called fecal-tagging materials (FTM), using positive-contrast tagging agents and laxative to facilitate polyp detection.

Methods

The proposed EC, EC_prop, integrates the gradient directional second derivative into material fraction model to preserve submerged soft tissue (ST) under FTM. Three-material fraction model is used to remove FTM and artifacts at air-tagging (AT) layers and T-junctions where air, ST, and FTM material meet simultaneously. Moreover, the proposed AT layer identification is used to distinguish AT layers from air-tissue-tagging (ATT) layers in order to preserve ATT layers during cleansing. The clinical evaluation on 467 3-Dimensional band view images was conducted by the abdominal radiologist using four grading levels of cleansing quality with five causes of low quality EC. The amount of the remaining artifacts at T-junctions was approximated from the results of EC_prop. The results from EC_prop were compared with the results from syngo.via Client 3.0 Software, EC_syngo, and the fast three-material modeling, EC_prev, using the preference of the radiologist. Two-tailed paired Wilcoxon signed rank test is used to indicate statistical significance.

Results

The average grade on cleansing quality is 2.89 out of 4. The artifacts at T-junctions from 86.94% of the test images can be removed, whereas artifacts at T-junctions from only 13.06% of the test images cannot be removed. For 13.06% of the test images, the results from EC_prop are more preferable to the results from EC_syngo (p<0.008). For all the test images, the results from EC_prop are more preferable to the results from EC_prev (p<0.001). Finally, the visual assessment shows that EC_prop can preserve ATT layers, submerged polyps and folds while EC_prev can preserve only submerged folds but fails to preserve ATT layers.

Conclusion

From our implementation, EC_prop can improve the performance of the existing EC, such that it can preserve ST, especially ATT layers and remove the artifacts at T-junctions which have never been proposed by any other methods before.

Progress in Fully Automated Abdominal CT Interpretation

OBJECTIVE

Automated analysis of abdominal CT has advanced markedly over just the last few years. Fully automated assessment of organs, lymph nodes, adipose tissue, muscle, bowel, spine, and tumors are some examples where tremendous progress has been made. Computer-aided detection of lesions has also improved dramatically.

CONCLUSION

This article reviews the progress and provides insights into what is in store in the near future for automated analysis for abdominal CT, ultimately leading to fully automated interpretation.

Context-specific method for detection of soft-tissue lesions in non-cathartic low-dose dual-energy CT colonography

In computed tomographic colonography (CTC), orally administered fecal-tagging agents can be used to indicate residual feces and fluid that could otherwise hide or imitate lesions on CTC images of the colon. Although the use of fecal tagging improves the detection accuracy of CTC, it can introduce image artifacts that may cause lesions that are covered by fecal tagging to have a different visual appearance than those not covered by fecal tagging. This can distort the values of image-based computational features, thereby reducing the accuracy of computer-aided detection (CADe). We developed a context-specific method that performs the detection of lesions separately on lumen regions covered by air and on those covered by fecal tagging, thereby facilitating the optimization of detection parameters separately for these regions and their detected lesion candidates to improve the detection accuracy of CADe. For pilot evaluation, the method was integrated into a dual-energy CADe (DE-CADe) scheme and evaluated by use of leave-one-patient-out evaluation on 66 clinical non-cathartic low-dose dual-energy CTC (DE-CTC) cases that were acquired at a low effective radiation dose and reconstructed by use of iterative image reconstruction. There were 22 colonoscopy-confirmed lesions ≥6 mm in size in 21 patients. The DE-CADe scheme detected 96% of the lesions at a median of 6 FP detections per patient. These preliminary results indicate that the use of context-specific detection can yield high detection accuracy of CADe in non-cathartic low-dose DE-CTC examinations.

Information-Preserving Pseudo-Enhancement Correction for Non-Cathartic Low-Dose Dual-Energy CT Colonography

In CT colonography (CTC), orally administered positive-contrast fecal-tagging agents can cause artificial elevation of the observed radiodensity of adjacent soft tissue. Such pseudo-enhancement makes it challenging to differentiate polyps and folds reliably from tagged materials, and it is also present in dual-energy CTC (DE-CTC). We developed a method that corrects for pseudo-enhancement on DE-CTC images without distorting the dual-energy information contained in the data. A pilot study was performed to evaluate the effect of the method visually and quantitatively by use of clinical non-cathartic low-dose DE-CTC data from 10 patients including 13 polyps covered partially or completely by iodine-based fecal tagging. The results indicate that the proposed method can be used to reduce the pseudo-enhancement distortion of DE-CTC images without losing material-specific dual-energy information. The method has potential application in improving the accuracy of automated image-processing applications, such as computer-aided detection and virtual bowel cleansing in CTC.

Application of Pseudo-enhancement Correction to Virtual Monochromatic CT Colonography

In CT colonography, orally administered positive-contrast fecal-tagging agents are used for differentiating residual fluid and feces from true lesions. However, the presence of high-density tagging agent in the colon can introduce erroneous artifacts, such as local pseudo-enhancement and beam-hardening, on the reconstructed CT images, thereby complicating reliable detection of soft-tissue lesions. In dual-energy CT colonography, such image artifacts can be reduced by the calculation of virtual monochromatic CT images, which provide more accurate quantitative attenuation measurements than conventional single-energy CT colonography. In practice, however, virtual monochromatic images may still contain some pseudo-enhancement artifacts, and efforts to minimize radiation dose may enhance such artifacts. In this study, we evaluated the effect of image-based pseudo-enhancement post-correction on virtual monochromatic images in standard-dose and low-dose dual-energy CT colonography. The mean CT values of the virtual monochromatic standard-dose CT images of 51 polyps and those of the virtual monochromatic low-dose CT images of 20 polyps were measured without and with the pseudo-enhancement correction. Statistically significant differences were observed between uncorrected and pseudo-enhancement-corrected images of polyps covered by fecal tagging in standard-dose CT (p < 0.001) and in low-dose CT (p < 0.05). The results indicate that image-based pseudo-enhancement post-correction can be useful for optimizing the performance of image-processing applications in virtual monochromatic CT colonography.

Fast three-material modeling with triple arch projection for electronic cleansing in CTC

In this paper, we propose a fast three-material modeling for electronic cleansing (EC) in computed tomographic colonography. Using a triple arch projection, our three-material modeling provides a very quick estimate of the three-material fractions to remove ridge-shaped artifacts at the T-junctions where air, soft-tissue (ST), and tagged residues (TRs) meet simultaneously. In our approach, colonic components including air, TR, the layer between air and TR, the layer between ST and TR (L(ST/TR)), and the T-junction are first segmented. Subsequently, the material fraction of ST for each voxel in L(ST/TR) and the T-junction is determined. Two-material fractions of the voxels in L(ST/TR) are derived based on a two-material transition model. On the other hand, three-material fractions of the voxels in the T-junction are estimated based on our fast three-material modeling with triple arch projection. Finally, the CT density value of each voxel is updated based on our fold-preserving reconstruction model. Experimental results using ten clinical datasets demonstrate that the proposed three-material modeling successfully removed the T-junction artifacts and clearly reconstructed the whole colon surface while preserving the submerged folds well. Furthermore, compared with the previous three-material transition model, the proposed three-material modeling resulted in about a five-fold increase in speed with the better preservation of submerged folds and the similar level of cleansing quality in T-junction regions.

Integration of 3D scale-based pseudo-enhancement correction and partial volume image segmentation for improving electronic colon cleansing in CT colonograpy

Orally administered tagging agents are usually used in CT colonography (CTC) to differentiate residual bowel content from native colonic structures. However, the high-density contrast agents tend to introduce pseudo-enhancement (PE) effect on neighboring soft tissues and elevate their observed CT attenuation value toward that of the tagged materials (TMs), which may result in an excessive electronic colon cleansing (ECC) since the pseudo-enhanced soft tissues are incorrectly identified as TMs. To address this issue, we integrated a 3D scale-based PE correction into our previous ECC pipeline based on the maximum a posteriori expectation-maximization partial volume (PV) segmentation. The newly proposed ECC scheme takes into account both the PE and PV effects that commonly appear in CTC images. We evaluated the new scheme on 40 patient CTC scans, both qualitatively through display of segmentation results, and quantitatively through radiologists' blind scoring (human observer) and computer-aided detection (CAD) of colon polyps (computer observer). Performance of the presented algorithm has shown consistent improvements over our previous ECC pipeline, especially for the detection of small polyps submerged in the contrast agents. The CAD results of polyp detection showed that 4 more submerged polyps were detected for our new ECC scheme over the previous one.

Fold-preserving electronic cleansing using a reconstruction model integrating material fractions and structural responses

In this paper, we propose an electronic cleansing method using a novel reconstruction model for removing tagged materials (TMs) in computed tomography (CT) images. To address the partial volume (PV) and pseudoenhancement (PEH) effects concurrently, material fractions and structural responses are integrated into a single reconstruction model. In our approach, colonic components including air, TM, an interface layer between air and TM, and an interface layer between soft-tissue (ST) and TM (IL ST/TM ) are first segmented. For each voxel in IL ST/TM, the material fractions of ST and TM are derived using a two-material transition model, and the structural response to identify the folds submerged in the TM is calculated by the rut-enhancement function based on the eigenvalue signatures of the Hessian matrix. Then, the CT density value of each voxel in IL ST/TM is reconstructed based on both the material fractions and structural responses. The material fractions remove the aliasing artifacts caused by a PV effect in IL ST/TM effectively while the structural responses avoid the erroneous cleansing of the submerged folds caused by the PEH effect. Experimental results using ten clinical datasets demonstrated that the proposed method showed higher cleansing quality and better preservation of submerged folds than the previous method, which was validated by the higher mean density values and fold preservation rates for manually segmented fold regions.

Matching 3-D prone and supine CT colonography scans using graphs

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.

Automated image-based colon cleansing for laxative-free CT colonography computer-aided polyp detection

PURPOSE

To evaluate the performance of a computer-aided detection (CAD) system for detecting colonic polyps at noncathartic computed tomography colonography (CTC) in conjunction with an automated image-based colon cleansing algorithm.

METHODS

An automated colon cleansing algorithm was designed to detect and subtract tagged-stool, accounting for heterogeneity and poor tagging, to be used in conjunction with a colon CAD system. The method is locally adaptive and combines intensity, shape, and texture analysis with probabilistic optimization. CTC data from cathartic-free bowel preparation were acquired for testing and training the parameters. Patients underwent various colonic preparations with barium or Gastroview in divided doses over 48 h before scanning. No laxatives were administered and no dietary modifications were required. Cases were selected from a polyp-enriched cohort and included scans in which at least 90% of the solid stool was visually estimated to be tagged and each colonic segment was distended in either the prone or supine view. The CAD system was run comparatively with and without the stool subtraction algorithm.

RESULTS

The dataset comprised 38 CTC scans from prone and/or supine scans of 19 patients containing 44 polyps larger than 10 mm (22 unique polyps, if matched between prone and supine scans). The results are robust on fine details around folds, thin-stool linings on the colonic wall, near polyps and in large fluid/stool pools. The sensitivity of the CAD system is 70.5% per polyp at a rate of 5.75 false positives/scan without using the stool subtraction module. This detection improved significantly (p = 0.009) after automated colon cleansing on cathartic-free data to 86.4% true positive rate at 5.75 false positives/scan.

CONCLUSIONS

An automated image-based colon cleansing algorithm designed to overcome the challenges of the noncathartic colon significantly improves the sensitivity of colon CAD by approximately 15%.

Improved computer-aided detection of small polyps in CT colonography using interpolation for curvature estimation

PURPOSE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Sources of false positives in computer-assisted CT colonography

The application of computer-aided detection (CAD) is expected to improve reader sensitivity and to reduce inter-observer variance in computed tomographic (CT) colonography. However, current CAD systems display a large number of false-positive (FP) detections. The reviewing of a large number of FP CAD detections increases interpretation time, and it may also reduce the specificity and/or sensitivity of a computer-assisted reader. Therefore, it is important to be aware of the patterns and pitfalls of FP CAD detections. This pictorial essay reviews common sources of FP CAD detections that have been observed in the literature and in our experiments in computer-assisted CT colonography. Also the recommended computer-assisted reading technique is described.

Polyp size measurement at CT colonography: what do we know and what do we need to know?

Polyp size is a critical biomarker for clinical management. Larger polyps have a greater likelihood of being or of becoming an adenocarcinoma. To balance the referral rate for polypectomy against the risk of leaving potential cancers in situ, sizes of 6 and 10 mm are increasingly being discussed as critical thresholds for clinical decision making (immediate polypectomy versus polyp surveillance) and have been incorporated into the consensus CT Colonography Reporting and Data System (C-RADS). Polyp size measurement at optical colonoscopy, pathologic examination, and computed tomographic (CT) colonography has been studied extensively but the reported precision, accuracy, and relative sizes have been highly variable. Sizes measured at CT colonography tend to lie between those measured at optical colonoscopy and pathologic evaluation. The size measurements are subject to a variety of sources of error associated with image acquisition, display, and interpretation, such as partial volume averaging, two- versus three-dimensional displays, and observer variability. This review summarizes current best practices for polyp size measurement, describes the role of automated size measurement software, discusses how to manage the measurement uncertainties, and identifies areas requiring further research.

Computer-aided Detection in Computed Tomography Colonography with Full Fecal Tagging: Comparison of Standalone Performance of 3 Automated Polyp Detection Systems

Purpose

We sought to compare the performance of 3 computer-aided detection (CAD) polyp algorithms in computed tomography colonography (CTC) with fecal tagging.

Methods

CTC data sets of 33 patients were retrospectively analysed by 3 different CAD systems: system 1, MedicSight; system 2, Colon CAD; and system 3, Polyp Enhanced View. The polyp database comprised 53 lesions, including 6 cases of colorectal cancer, and was established by consensus reading and comparison with colonoscopy. Lesions ranged from 6-40 mm, with 25 lesions larger than 10 mm in size. Detection and false-positive (FP) rates were calculated.

Results

CAD systems 1 and 2 could be set to have varying sensitivities with higher FP rates for higher sensitivity levels. Sensitivities for system 1 ranged from 73%–94% for all lesions (78%–100% for lesions ≥10 mm) and, for system 2, from 64%–94% (78%–100% for lesions ≥10 mm). System 3 reached an overall sensitivity of 76% (100% for lesions ≥10 mm). The mean FP rate per patient ranged from 8–32 for system 1, from 1–8 for system 2, and was 5 for system 3. At the highest sensitivity level for all polyps (94%), system 2 showed a statistically significant lower FP rate compared with system 1 ( P = .001). When analysing lesions ≥10 mm, system 3 had significantly fewer FPs than systems 1 and 2 ( P < .012).

Conclusions

Standalone CTC-CAD analysis in the selected patient collective showed the 3 systems tested to have a variable but overall promising performance with respect to sensitivity and the FP rate.

Improving the accuracy of CTC interpretation: computer-aided detection

Computer-aided polyp detection aims to improve the accuracy of the colonography interpretation. The computer searches the colonic wall to look for polyplike protrusions and presents a list of suspicious areas to a physician for further analysis. Computer-aided polyp detection has developed rapidly in the past decade in the laboratory setting and has sensitivities comparable with those of experts. Computer-aided polyp detection tends to help inexperienced readers more than experienced ones and may also lead to small reductions in specificity. In its currently proposed use as an adjunct to standard image interpretation, computer-aided polyp detection serves as a spellchecker rather than an efficiency enhancer.

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

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Nonlinear regression-based method for pseudoenhancement correction in CT colonography

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 > or =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.

Virtual tagging for laxative-free CT colonography: pilot evaluation

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 > or = 10 mm (n = 4) with 3.8 false positives per patient (per two CT scan volumes) and 90% for lesions > or = 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).

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.

An EM approach to MAP solution of segmenting tissue mixture percentages with application to CT-based virtual colonoscopy

Electronic colon cleansing (ECC) is an emerging technique developed to segment the colon lumen from a patient's abdominal computed tomography colonography (CTC) images. However, the residue stool and fluid tagged by contrast materials as well as mixed tissue distribution with partial volume (PV) effect impose several challenges for ECC, resulting in incomplete and overcomplete cleansings. To address the PV effect, this work investigated an improved maximum a posteriori expectation-maximization (MAP-EM) image segmentation algorithm which simultaneously estimates tissue mixture percentages within each image voxel and statistical model parameters for the tissue distribution. Given the segmented tissue mixture information beyond the image voxel level, not only the PV effect has been satisfactorily addressed as a particular case of tissue mixture problem, but incomplete and overcomplete ECC causes could also be maximally avoided. For clinical application to CTC that involves several issues transferring from theoretical analysis to practical validation, an innovative initialization procedure and refined estimation strategy were proposed to build an ECC pipeline based on the MAP-EM segmentation. The pipeline was evaluated based on 52 patient CTC studies, downloaded from the website of the Virtual Colonoscopy Screening Resource Center, by two radiologists. A noticeable improvement over the authors' previous ECC pipeline was documented. Several typical cases were also presented to show visually the improved performance of the presented ECC pipeline.

A Theoretical Solution to MAP-EM Partial Volume Segmentation of Medical Images

Voxels near tissue borders in medical images contain useful clinical information, but are subject to severe partial volume (PV) effect, which is a major cause of imprecision in quantitative volumetric and texture analysis. When modeling each tissue type as a conditionally independent Gaussian distribution, the tissue mixture fractions in each voxel via the modeled unobservable random processes of the underlying tissue types can be estimated by maximum a posteriori expectation-maximization (MAP-EM) algorithm in an iterative manner. This paper presents, based on the assumption that PV effect could be fully described by a tissue mixture model, a theoretical solution to the MAP-EM segmentation algorithm, as opposed to our previous approximation which simplified the posteriori cost function as a quadratic term. It was found out that the theoretically-derived solution existed in a set of high-order non-linear equations. Despite of the induced computational complexity when seeking for optimum numerical solutions to non-linear equations, potential gains in robustness, consistency and quantitative precision were noticed. Results from both synthetic digital phantoms and real patient bladder magnetic resonance images were presented, demonstrating the accuracy and efficiency of the presented theoretical MAP-EM solution.