Comparison of the performance of conventional and spectral-based tagged stool cleansing algorithms at CT colonography

OBJECTIVES

To compare the performance of conventional versus spectral-based electronic stool cleansing for iodine-tagged CT colonography (CTC) using a dual-layer spectral detector scanner.

METHODS

We retrospectively evaluated iodine contrast stool-tagged CTC scans of 30 consecutive patients (mean age: 69 ± 8 years) undergoing colorectal cancer screening obtained on a dual-layer spectral detector CT scanner. One reader identified locations of electronic cleansing artifacts (n = 229) on conventional and spectral cleansed images. Three additional independent readers evaluated these locations using a conventional cleansing algorithm (Intellispace Portal) and two experimental spectral cleansing algorithms (i.e., fully transparent and translucent tagged stool). For each cleansed image set, readers recorded the severity of over- and under-cleansing artifacts on a 5-point Likert scale (0 = none to 4 = severe) and readability compared to uncleansed images. Wilcoxon's signed-rank tests were used to assess artifact severity, type, and readability (worse, unchanged, or better).

RESULTS

Compared with conventional cleansing (66% score ≥ 2), the severity of overall cleansing artifacts was lower in transparent (60% score ≥ 2, p = 0.011) and translucent (50% score ≥ 2, p < 0.001) spectral cleansing. Under-cleansing artifact severity was lower in transparent (49% score ≥ 2, p < 0.001) and translucent (39% score ≥ 2, p < 0.001) spectral cleansing compared with conventional cleansing (60% score ≥ 2). Over-cleansing artifact severity was worse in transparent (17% score ≥ 2, p < 0.001) and translucent (14% score ≥ 2, p = 0.023) spectral cleansing compared with conventional cleansing (9% score ≥ 2). Overall readability was significantly improved in transparent (p < 0.001) and translucent (p < 0.001) spectral cleansing compared with conventional cleansing.

CONCLUSIONS

Spectral cleansing provided more robust electronic stool cleansing of iodine-tagged stool at CTC than conventional cleansing.

KEY POINTS

• Spectral-based electronic cleansing of tagged stool at CT colonography provides higher quality images with less perception of artifacts than does conventional cleansing. • Spectral-based electronic cleansing could potentially advance minimally cathartic approach for CT colonography. Further clinical trials are warranted.

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.

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.

Domain-Based Analysis of Colon Polyp in CT Colonography Using Image-Processing Techniques

Background: The purpose of the research was to improve the polyp detection accuracy in CT Colonography (CTC) through effective colon segmentation, removal of tagged fecal matter through Electronic Cleansing (EC), and measuring the smaller polyps. Methods: An improved method of boundary-based semi-automatic colon segmentation with the knowledge of colon distension, an adaptive multistep method for the virtual cleansing of segmented colon based on the knowledge of Hounsfield Units, and an automated method of smaller polyp measurement using skeletonization technique have been implemented. Results: The techniques were evaluated on 40 CTC dataset. The segmentation method was able to delineate the colon wall accurately. The submerged colonic structures were preserved without soft tissue erosion, pseudo enhanced voxels were corrected, and the air-contrast layer was removed without losing the adjacent tissues. The smaller polyp of size less than <10mm was detected correctly. The results were statistically validated qualitatively and quantitatively. Segmented colons were validated through volumetric overlap computation, and accuracy of 95.826±0.6854% was achieved. In polyp measurement, the paired t-test method was applied to compare the difference with ground truth and at α=5%, t=0.9937 and p=0.098 was achieved. The statistical values of TPR=90%, TNR=82.3% and accuracy=88.31% were achieved. Conclusion: An automated system of polyp measurement has been developed starting from colon segmentation to improve the existing CTC solutions. The analysis of domain-based approach of polyp has given good results. A prototype software, which can be used as a low-cost polyp diagnosis tool, has been developed.

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.

Automatic Electronic Cleansing in Computed Tomography Colonography Images using Domain Knowledge

Electronic cleansing is an image post processing technique in which the tagged colonic content is subtracted from colon using CTC images. There are post processing artefacts, like: 1) soft tissue degradation; 2) incomplete cleansing; 3) misclassification of polyp due to pseudo enhanced voxels; and 4) pseudo soft tissue structures. The objective of the study was to subtract the tagged colonic content without losing the soft tissue structures. This paper proposes a novel adaptive method to solve the first three problems using a multi-step algorithm. It uses a new edge model-based method which involves colon segmentation, priori information of Hounsfield units (HU) of different colonic contents at specific tube voltages, subtracting the tagging materials, restoring the soft tissue structures based on selective HU, removing boundary between air-contrast, and applying a filter to clean minute particles due to improperly tagged endoluminal fluids which appear as noise. The main finding of the study was submerged soft tissue structures were absolutely preserved and the pseudo enhanced intensities were corrected without any artifact. The method was implemented with multithreading for parallel processing in a high performance computer. The technique was applied on a fecal tagged dataset (30 patients) where the tagging agent was not completely removed from colon. The results were then qualitatively validated by radiologists for any image processing artifacts.

A Markov Random Field orientation prior for electronic cleansing in CT Colonography

Tagging of the bowel content with an oral contrast facilitates CT Colonography with limited bowel preparation. Electronic colon cleansing (ECC) reconstructs the colon lumen, devoid of feces from a CT scan acquired with fecal fluid tagging. A popular method to estimate the stool composition in an image (with the purpose of removing it) is the well-established Expectation Maximization (EM) method. The tagged fluid residue appears as a contrast enhanced region with a largely horizontal interface with air above it. One of the issues is the partial volume (PV) effect that creates voxels with attenuations similar to that of the colon wall at the boundary of air and tagged fluid. We present here, a novel orientation prior formulated as a Markov Random Field that is included as part of the EM tissue segmentation framework to mitigate this PV effect at the air and tagged fluid layer. We show quantitative results on a simple synthetic dataset and qualitative results on patient data that highlight improvements due to the inclusion of the orientation prior.

Electronic cleansing for dual-energy CT colonography based on material decomposition and virtual monochromatic imaging

CT colonography (CTC) uses orally administered fecal-tagging agents to enhance retained fluid and feces that would otherwise obscure or imitate polyps on CTC images. To visualize the complete region of colon without residual materials, electronic cleansing (EC) can be used to perform virtual subtraction of the tagged materials from CTC images. However, current EC methods produce subtraction artifacts and they can fail to subtract unclearly tagged feces. We developed a novel multi-material EC (MUMA-EC) method that uses dual-energy CTC (DE-CTC) and machine-learning methods to improve the performance of EC. In our method, material decomposition is performed to calculate water-iodine decomposition images and virtual monochromatic (VIM) images. Using the images, a random forest classifier is used to label the regions of lumen air, soft tissue, fecal tagging, and their partial-volume boundaries. The electronically cleansed images are synthesized from the multi-material and VIM image volumes. For pilot evaluation, we acquired the clinical DE-CTC data of 7 patients. Preliminary results suggest that the proposed MUMA-EC method is effective and that it minimizes the three types of image artifacts that were present in previous EC methods.

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.

Electronic cleansing for 24-h limited bowel preparation CT colonography using principal curvature flow

CT colonography (CTC) is one of the recommended methods for colorectal cancer screening. The subject's preparation is one of the most burdensome aspects of CTC with a cathartic bowel preparation. Tagging of the bowel content with an oral contrast medium facilitates CTC with limited bowel preparation. Unfortunately, such preparations adversely affect the 3-D image quality. Thus far, data acquired after very limited bowel preparation were evaluated with a 2-D reading strategy only. Existing cleansing algorithms do not work sufficiently well to allow a primary 3-D reading strategy. We developed an electronic cleansing algorithm, aimed to realize optimal 3-D image quality for low-dose CTC with 24-h limited bowel preparation. The method employs a principal curvature flow algorithm to remove heterogeneities within poorly tagged fecal residue. In addition, a pattern recognition-based approach is used to prevent polyp-like protrusions on the colon surface from being removed by the method. Two experts independently evaluated 40 CTC cases by means of a primary 2-D approach without involvement of electronic cleansing as well as by a primary 3-D method after electronic cleansing. The data contained four variations of 24-h limited bowel preparation and was based on a low radiation dose scanning protocol. The sensitivity for lesions ≥ 6 mm was significantly higher for the primary 3-D reading strategy (84%) than for the primary 2-D reading strategy (68%) (p = 0.031). The reading time was increased from 5:39 min (2-D) to 7:09 min (3-D) (p = 0.005); the readers' confidence was reduced from 2.3 (2-D) to 2.1 (3-D) ( p = 0.013) on a three-point Likert scale. Polyp conspicuity for cleansed submerged lesions was similar to not submerged lesions (p = 0.06). To our knowledge, this study is the first to describe and clinically validate an electronic cleansing algorithm that facilitates low-dose CTC with 24-h limited bowel preparation.

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.

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%.

Mosaic decomposition: an electronic cleansing method for inhomogeneously tagged regions in noncathartic CT colonography

Electronic cleansing (EC) is a method that segments fecal material tagged by an X-ray-opaque oral contrast agent in computed tomographic colonography (CTC) images, and effectively removes the material for digitally cleansing the colon. In this study, we developed a novel EC method, called mosaic decomposition (MD), for reduction of the artifacts due to incomplete cleansing of inhomogeneously tagged fecal material in CTC images, especially in noncathartic CTC images. In our approach, the entire colonic region, including the residual fecal regions, was first decomposed into a set of local homogeneous regions, called tiles, after application of a 3-D watershed transform to the CTC images. Each tile was then subjected to a single-class support vector machine (SVM) classifier for soft-tissue discrimination. The feature set of the soft-tissue SVM classifier was selected by a genetic algorithm (GA). A scalar index, called a soft-tissue likelihood, is formulated for differentiation of the soft-tissue tiles from those of other materials. Then, EC based on MD, called MD-cleansing, is performed by first initializing of the level-set front with the classified tagged regions; the front is then evolved by use of a speed function that was designed, based on the soft-tissue index, to reserve the submerged soft-tissue structures while suppressing the residual fecal regions. The performance of the MD-cleansing method was evaluated by use of a phantom and of clinical cases. In the phantom evaluation, our MD-cleansing was trained with the supine (prone) scan and tested on the prone (supine) scan, respectively. In both cases, the sensitivity and specificity of classification were 100%. The average cleansing ratio was 90.6%, and the soft-tissue preservation ratio was 97.6%. In the clinical evaluation, 10 noncathartic CTC cases (20 scans) were collected, and the ground truth of a total of 2095 tiles was established by manual assignment of a material class to each tile. Five cases were randomly selected for training GA/SVM, and the remaining five cases were used for testing. The overall sensitivity and specificity of the proposed classification scheme were 97.1% and 85.3%, respectively, and the accuracy was 94.6%. The area under the ROC curve (Az) was 0.96. Our results indicated that the use of MD-cleansing substantially improved the effectiveness of our EC method in the reduction of incomplete cleansing artifacts.

Semi-automated segmentation of the sigmoid and descending colon for radiotherapy planning using the fast marching method

A fast and accurate segmentation of organs at risk, such as the healthy colon, would be of benefit for planning of radiotherapy, in particular in an adaptive scenario. For the treatment of pelvic tumours, a great challenge is the segmentation of the most adjacent and sensitive parts of the gastrointestinal tract, the sigmoid and descending colon. We propose a semi-automated method to segment these bowel parts using the fast marching (FM) method. Standard 3D computed tomography (CT) image data obtained from routine radiotherapy planning were used. Our pre-processing steps distinguish the intestine, muscles and air from connective tissue. The core part of our method separates the sigmoid and descending colon from the muscles and other segments of the intestine. This is done by utilizing the ability of the FM method to compute a specified minimal energy functional integrated along a path, and thereby extracting the colon centre line between user-defined control points in the sigmoid and descending colon. Further, we reconstruct the tube-shaped geometry of the sigmoid and descending colon by fitting ellipsoids to points on the path and by adding adjacent voxels that are likely voxels belonging to these bowel parts. Our results were compared to manually outlined sigmoid and descending colon, and evaluated using the Dice coefficient (DC). Tests on 11 patients gave an average DC of 0.83 (+/-0.07) with little user interaction. We conclude that the proposed method makes it possible to fast and accurately segment the sigmoid and descending colon from routine CT image data.