Registration of central paths and colonic polyps between supine and prone scans in computed tomography colonography: Pilot study

Corresponding Supine and Prone Colon Visualization Using Eigenfunction Analysis and Fold Modeling

We present a method for registration and visualization of corresponding supine and prone virtual colonoscopy scans based on eigenfunction analysis and fold modeling. In virtual colonoscopy, CT scans are acquired with the patient in two positions, and their registration is desirable so that physicians can corroborate findings between scans. Our algorithm performs this registration efficiently through the use of Fiedler vector representation (the second eigenfunction of the Laplace-Beltrami operator). This representation is employed to first perform global registration of the two colon positions. The registration is then locally refined using the haustral folds, which are automatically segmented using the 3D level sets of the Fiedler vector. The use of Fiedler vectors and the segmented folds presents a precise way of visualizing corresponding regions across datasets and visual modalities. We present multiple methods of visualizing the results, including 2D flattened rendering and the corresponding 3D endoluminal views. The precise fold modeling is used to automatically find a suitable cut for the 2D flattening, which provides a less distorted visualization. Our approach is robust, and we demonstrate its efficiency and efficacy by showing matched views on both the 2D flattened colons and in the 3D endoluminal view. We analytically evaluate the results by measuring the distance between features on the registered colons, and we also assess our fold segmentation against 20 manually labeled datasets. We have compared our results analytically to previous methods, and have found our method to achieve superior results. We also prove the hot spots conjecture for modeling cylindrical topology using Fiedler vector representation, which allows our approach to be used for general cylindrical geometry modeling and feature extraction.

Haustral loop extraction for CT colonography using geodesics

Purpose

The human colon has complex geometric structures because of its haustral folds, which are thin flat protrusions on the colon wall. The haustral loop is the curve (approximately triangular in shape) that encircles the highly convex region of the haustral fold, and is regarded as the natural landmark of the colon, intersecting the longitude of the colon in the middle. Haustral loop extraction can assist in reducing the structural complexity of the colon, and the loops can also serve as anatomic markers for computed tomographic colonography (CTC). Moreover, haustral loop sectioning of the colon can help with the performance of precise prone–supine registration.

Methods

We propose an accurate approach of extracting haustral loops for CT virtual colonoscopy based on geodesics. First, the longitudinal geodesic (LG) connecting the start and end points is tracked by the geodesic method and the colon is cut along the LG. Second, key points are extracted from the LG, after which paired points that are used for seeking the potential haustral loops are calculated according to the key points. Next, for each paired point, the shortest distance (geodesic line) between the paired points twice is calculated, namely one on the original surface and the other on the cut surface. Then, the two geodesics are combined to form a potential haustral loop. Finally, erroneous and nonstandard potential loops are removed.

Results

To evaluate the haustral loop extraction algorithm, we first utilized the algorithm to extract the haustral loops. Then, we let the clinicians determine whether the haustral loops were correct and then identify the missing haustral loops. The extraction algorithm successfully detected 91.87% of all of the haustral loops with a very low false positive rate.

Conclusions

We believe that haustral loop extraction may benefit many post-procedures in CTC, such as supine–prone registration, computer-aided diagnosis, and taenia coli extraction.

CT colonography: clinical evaluation of a method for automatic coregistration of polyps at follow-up surveillance studies

PURPOSE

To evaluate the accuracy of a method of automatic coregistration of the endoluminal surfaces at computed tomographic (CT) colonography performed on separate occasions to facilitate identification of polyps in patients undergoing polyp surveillance.

MATERIALS AND METHODS

Institutional review board and HIPAA approval were obtained. A registration algorithm that was designed to coregister the coordinates of endoluminal colonic surfaces on images from prone and supine CT colonographic acquisitions was used to match polyps in sequential studies in patients undergoing polyp surveillance. Initial and follow-up CT colonographic examinations in 26 patients (35 polyps) were selected and the algorithm was tested by means of two methods, the longitudinal method (polyp coordinates from the initial prone and supine acquisitions were used to identify the expected polyp location automatically at follow-up CT colonography) and the consistency method (polyp coordinates from the initial supine acquisition were used to identify polyp location on images from the initial prone acquisition, then on those for follow-up prone and follow-up supine acquisitions). Two observers measured the Euclidean distance between true and expected polyp locations, and mean per-patient registration accuracy was calculated. Segments with and without collapse were compared by using the Kruskal-Wallace test, and the relationship between registration error and temporal separation was investigated by using the Pearson correlation.

RESULTS

Coregistration was achieved for all 35 polyps by using both longitudinal and consistency methods. Mean ± standard deviation Euclidean registration error for the longitudinal method was 17.4 mm ± 12.1 and for the consistency method, 26.9 mm ± 20.8. There was no significant difference between these results and the registration error when prone and supine acquisitions in the same study were compared (16.9 mm ± 17.6; P = .451).

CONCLUSION

Automatic endoluminal coregistration by using an algorithm at initial CT colonography allowed prediction of endoluminal polyp location at subsequent CT colonography, thereby facilitating detection of known polyps in patients undergoing CT colonographic surveillance.

3D Reconstruction of virtual colon structures from colonoscopy images

This paper presents the first fully automated reconstruction technique of 3D virtual colon segments from individual colonoscopy images. It is the basis of new software applications that may offer great benefits for improving quality of care for colonoscopy patients. For example, a 3D map of the areas inspected and uninspected during colonoscopy can be shown on request of the endoscopist during the procedure. The endoscopist may revisit the suggested uninspected areas to reduce the chance of missing polyps that reside in these areas. The percentage of the colon surface seen by the endoscopist can be used as a coarse objective indicator of the quality of the procedure. The derived virtual colon models can be stored for post-procedure training of new endoscopists to teach navigation techniques that result in a higher level of procedure quality. Our technique does not require a prior CT scan of the colon or any global positioning device. Our experiments on endoscopy images of an Olympus synthetic colon model reveal encouraging results with small average reconstruction errors (4.1 mm for the fold depths and 12.1 mm for the fold circumferences).

CT colonography: external clinical validation of an algorithm for computer-assisted prone and supine registration

PURPOSE

To perform external validation of a computer-assisted registration algorithm for prone and supine computed tomographic (CT) colonography and to compare the results with those of an existing centerline method.

MATERIALS AND METHODS

All contributing centers had institutional review board approval; participants provided informed consent. A validation sample of CT colonographic examinations of 51 patients with 68 polyps (6-55 mm) was selected from a publicly available, HIPAA compliant, anonymized archive. No patients were excluded because of poor preparation or inadequate distension. Corresponding prone and supine polyp coordinates were recorded, and endoluminal surfaces were registered automatically by using a computer algorithm. Two observers independently scored three-dimensional endoluminal polyp registration success. Results were compared with those obtained by using the normalized distance along the colonic centerline (NDACC) method. Pairwise Wilcoxon signed rank tests were used to compare gross registration error and McNemar tests were used to compare polyp conspicuity.

RESULTS

Registration was possible in all 51 patients, and 136 paired polyp coordinates were generated (68 polyps) to test the algorithm. Overall mean three-dimensional polyp registration error (mean ± standard deviation, 19.9 mm ± 20.4) was significantly less than that for the NDACC method (mean, 27.4 mm ± 15.1; P = .001). Accuracy was unaffected by colonic segment (P = .76) or luminal collapse (P = .066). During endoluminal review by two observers (272 matching tasks, 68 polyps, prone to supine and supine to prone coordinates), 223 (82%) polyp matches were visible (120° field of view) compared with just 129 (47%) when the NDACC method was used (P < .001). By using multiplanar visualization, 48 (70%) polyps were visible after scrolling ± 15 mm in any multiplanar axis compared with 16 (24%) for NDACC (P < .001).

CONCLUSION

Computer-assisted registration is more accurate than the NDACC method for mapping the endoluminal surface and matching the location of polyps in corresponding prone and supine CT colonographic acquisitions.

Endoluminal surface registration for CT colonography using haustral fold matching

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Novel haustral fold matching algorithm.

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Achieves 96.1% mean accuracy over 1743 reference points in 17 CTC datasets.

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New initialisation to non-rigid intensity-based surface registration method.

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Full method shows 6.0 mm mean error.

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Use of initialisation shows significant improvement (p < 0.001).

Computed Tomographic (CT) colonography is a technique used for the detection of bowel cancer or potentially precancerous polyps. The procedure is performed routinely with the patient both prone and supine to differentiate fixed colonic pathology from mobile faecal residue. Matching corresponding locations is difficult and time consuming for radiologists due to colonic deformations that occur during patient repositioning.

We propose a novel method to establish correspondence between the two acquisitions automatically. The problem is first simplified by detecting haustral folds using a graph cut method applied to a curvature-based metric applied to a surface mesh generated from segmentation of the colonic lumen. A virtual camera is used to create a set of images that provide a metric for matching pairs of folds between the prone and supine acquisitions. Image patches are generated at the fold positions using depth map renderings of the endoluminal surface and optimised by performing a virtual camera registration over a restricted set of degrees of freedom. The intensity difference between image pairs, along with additional neighbourhood information to enforce geometric constraints over a 2D parameterisation of the 3D space, are used as unary and pair-wise costs respectively, and included in a Markov Random Field (MRF) model to estimate the maximum a posteriori fold labelling assignment.

The method achieved fold matching accuracy of 96.0% and 96.1% in patient cases with and without local colonic collapse. Moreover, it improved upon an existing surface-based registration algorithm by providing an initialisation. The set of landmark correspondences is used to non-rigidly transform a 2D source image derived from a conformal mapping process on the 3D endoluminal surface mesh. This achieves full surface correspondence between prone and supine views and can be further refined with an intensity based registration showing a statistically significant improvement (p < 0.001), and decreasing mean error from 11.9 mm to 6.0 mm measured at 1743 reference points from 17 CTC datasets.

Predicting polyp location on optical colonoscopy from CT colonography by minimal-energy curve modeling of the colonoscope path

The ability to accurately locate a polyp found on computed tomographic colonography (CTC) at subsequent optical colonoscopy (OC) is an important task in colorectal cancer screening. We present a method to more accurately match polyp locations at CTC and OC. A colonoscope was modeled as a flexible tube with negligible stretch and minimal strain. The path of the colonoscope was estimated using a minimal-energy curve method. The energy function was defined and optimized by a subdivision scheme. The prediction of polyp locations at OC from CTC was converted to an optimization problem. The prediction performance was evaluated on 134 polyps by comparing the predicted with the true polyp locations at OC. The method can accurately predict polyp locations at OC to within ±0.5 colonoscope mark (5 cm) for more than 58% of polyps and to within ±1 colonoscope mark (10 cm) for more than 96% of polyps, significantly improving upon previously published methods. This method can be easily incorporated into routine OC practice and allow the colonoscopist to begin the examination by targeting locations of potential polyps found at CTC.

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 teniae coli detection and identification on computed tomographic colonography

PURPOSE

Computed tomographic colonography (CTC) is a minimally invasive technique for colonic polyps and cancer screening. Teniae coli are three bands of longitudinal smooth muscle on the colon surface. Teniae coli are important anatomically meaningful landmarks on human colon. In this paper, the authors propose an automatic teniae coli detection method for CT colonography.

METHODS

The original CTC slices are first segmented and reconstructed to a 3D colon surface. Then, the 3D colon surface is unfolded using a reversible projection technique. After that the unfolded colon is projected to a 2D height map. The teniae coli are detected using the height map and then reversely projected back to the 3D colon. Since teniae are located at the junctions where the haustral folds meet, the authors apply 2D Gabor filter banks to extract features of haustral folds. The maximum response of the filter banks is then selected as the feature image. The fold centers are then identified based on local maxima and thresholding on the feature image. Connecting the fold centers yields a path of the folds. Teniae coli are extracted as lines running between the fold paths. The authors used the spatial relationship between ileocecal valve (ICV) and teniae mesocolica (TM) to identify the TM, then the teniae omentalis (TO) and the teniae libera (TL) can be identified subsequently.

RESULTS

The authors tested the proposed method on 47 cases of 37 patients, 10 of the patients with both supine and prone CT scans. The proposed method yielded performance with an average normalized root mean square error (RMSE) ( ± standard deviation [95% confidence interval]) of 4.87% ( ± 2.93%, [4.05% 5.69%]).

CONCLUSIONS

The proposed fully-automated teniae coli detection and identification method is accurate and promising for future clinical applications.

A robust and efficient approach to detect 3D rectal tubes from CT colonography

PURPOSE

The rectal tube (RT) is a common source of false positives (FPs) in computer-aided detection (CAD) systems for CT colonography. A robust and efficient detection of RT can improve CAD performance by eliminating such "obvious" FPs and increase radiologists' confidence in CAD.

METHODS

In this paper, we present a novel and robust bottom-up approach to detect the RT. Probabilistic models, trained using kernel density estimation on simple low-level features, are employed to rank and select the most likely RT tube candidate on each axial slice. Then, a shape model, robustly estimated using random sample consensus (RANSAC), infers the global RT path from the selected local detections. Subimages around the RT path are projected into a subspace formed from training subimages of the RT. A quadratic discriminant analysis (QDA) provides a classification of a subimage as RT or non-RT based on the projection. Finally, a bottom-top clustering method is proposed to merge the classification predictions together to locate the tip position of the RT.

RESULTS

Our method is validated using a diverse database, including data from five hospitals. On a testing data with 21 patients (42 volumes), 99.5% of annotated RT paths have been successfully detected. Evaluated with CAD, 98.4% of FPs caused by the RT have been detected and removed without any loss of sensitivity.

CONCLUSIONS

The proposed method demonstrates a high detection rate of the RT path, and when tested in a CAD system, reduces FPs caused by the RT without the loss of sensitivity.

Registration of the endoluminal surfaces of the colon derived from prone and supine CT colonography

PURPOSE

Computed tomographic (CT) colonography is a relatively new technique for detecting bowel cancer or potentially precancerous polyps. CT scanning is combined with three-dimensional (3D) image reconstruction to produce a virtual endoluminal representation similar to optical colonoscopy. Because retained fluid and stool can mimic pathology, CT data are acquired with the bowel cleansed and insufflated with gas and patient in both prone and supine positions. Radiologists then match visually endoluminal locations between the two acquisitions in order to determine whether apparent pathology is real or not. This process is hindered by the fact that the colon, essentially a long tube, can undergo considerable deformation between acquisitions. The authors present a novel approach to automatically establish spatial correspondence between prone and supine endoluminal colonic surfaces after surface parameterization, even in the case of local colon collapse.

METHODS

The complexity of the registration task was reduced from a 3D to a 2D problem by mapping the surfaces extracted from prone and supine CT colonography onto a cylindrical parameterization. A nonrigid cylindrical registration was then performed to align the full colonic surfaces. The curvature information from the original 3D surfaces was used to determine correspondence. The method can also be applied to cases with regions of local colonic collapse by ignoring the collapsed regions during the registration.

RESULTS

Using a development set, suitable parameters were found to constrain the cylindrical registration method. Then, the same registration parameters were applied to a different set of 13 validation cases, consisting of 8 fully distended cases and 5 cases exhibiting multiple colonic collapses. All polyps present were well aligned, with a mean (+/- std. dev.) registration error of 5.7 (+/- 3.4) mm. An additional set of 1175 reference points on haustral folds spread over the full endoluminal colon surfaces resulted in an error of 7.7 (+/- 7.4) mm. Here, 82% of folds were aligned correctly after registration with a further 15% misregistered by just onefold.

CONCLUSIONS

The proposed method reduces the 3D registration task to a cylindrical registration representing the endoluminal surface of the colon. Our algorithm uses surface curvature information as a similarity measure to drive registration to compensate for the large colorectal deformations that occur between prone and supine data acquisitions. The method has the potential to both enhance polyp detection and decrease the radiologist's interpretation time.

Registration under topological change for CT colonography

Computed tomography (CT) colonography is a minimally invasive screening technique for colorectal polyps, in which X-ray CT images of the distended colon are acquired, usually in the prone and supine positions of a single patient. Registration of segmented colon images from both positions will be useful for computer-assisted polyp detection. We have previously presented algorithms for registration of the prone and supine colons when both are well distended and there is a single connected lumen. However, due to inadequate bowel preparation or peristalsis, there may be collapsed segments in one or both of the colon images resulting in a topological change in the images. Such changes make deformable registration of the colon images difficult, and at present, there are no registration algorithms that can accommodate them. In this paper, we present an algorithm that can perform volume registration of prone/supine colon images in the presence of a topological change. For this purpose, 3-D volume images are embedded as a manifold in a 4-D space, and the manifold is evolved for nonrigid registration. Experiments using data from 24 patients show that the proposed method achieves good registration results in both the shape alignment of topologically different colon images from a single patient and the polyp location estimation between supine and prone colon images.

Supine and prone colon registration using quasi-conformal mapping

In virtual colonoscopy, CT scans are typically acquired with the patient in both supine (facing up) and prone (facing down) positions. The registration of these two scans is desirable so that the user can clarify situations or confirm polyp findings at a location in one scan with the same location in the other, thereby improving polyp detection rates and reducing false positives. However, this supine-prone registration is challenging because of the substantial distortions in the colon shape due to the patient's change in position. We present an efficient algorithm and framework for performing this registration through the use of conformal geometry to guarantee that the registration is a diffeomorphism (a one-to-one and onto mapping). The taeniae coli and colon flexures are automatically extracted for each supine and prone surface, employing the colon geometry. The two colon surfaces are then divided into several segments using the flexures, and each segment is cut along a taenia coli and conformally flattened to the rectangular domain using holomorphic differentials. The mean curvature is color encoded as texture images, from which feature points are automatically detected using graph cut segmentation, mathematic morphological operations, and principal component analysis. Corresponding feature points are found between supine and prone and are used to adjust the conformal flattening to be quasi-conformal, such that the features become aligned. We present multiple methods of visualizing our results, including 2D flattened rendering, corresponding 3D endoluminal views, and rendering of distortion measurements. We demonstrate the efficiency and efficacy of our registration method by illustrating matched views on both the 2D flattened colon images and in the 3D volume rendered colon endoluminal view. We analytically evaluate the correctness of the results by measuring the distance between features on the registered colons.

Registration of prone and supine CT colonography scans using correlation optimized warping and canonical correlation analysis

PURPOSE

In computed tomographic colonography (CTC), a patient will be scanned twice-Once supine and once prone-to improve the sensitivity for polyp detection. To assist radiologists in CTC reading, in this paper we propose an automated method for colon registration from supine and prone CTC scans.

METHODS

We propose a new colon centerline registration method for prone and supine CTC scans using correlation optimized warping (COW) and canonical correlation analysis (CCA) based on the anatomical structure of the colon. Four anatomical salient points on the colon are first automatically distinguished. Then correlation optimized warping is applied to the segments defined by the anatomical landmarks to improve the global registration based on local correlation of segments. The COW method was modified by embedding canonical correlation analysis to allow multiple features along the colon centerline to be used in our implementation.

RESULTS

We tested the COW algorithm on a CTC data set of 39 patients with 39 polyps (19 training and 20 test cases) to verify the effectiveness of the proposed COW registration method. Experimental results on the test set show that the COW method significantly reduces the average estimation error in a polyp location between supine and prone scans by 67.6%, from 46.27 +/- 52.97 to 14.98 mm +/- 11.41 mm, compared to the normalized distance along the colon centerline algorithm (p < 0.01).

CONCLUSIONS

The proposed COW algorithm is more accurate for the colon centerline registration compared to the normalized distance along the colon centerline method and the dynamic time warping method. Comparison results showed that the feature combination of z-coordinate and curvature achieved lowest registration error compared to the other feature combinations used by COW. The proposed method is tolerant to centerline errors because anatomical landmarks help prevent the propagation of errors across the entire colon centerline.

Deformable registration of supine and prone colons for computed tomographic colonography

Computed tomographic colonography is a minimally invasive technique for detecting colorectal polyps and colon cancer. Most computed tomographic colonography protocols acquire both prone and supine images to improve the visualization of the lumen wall, reduce false-positives, and improve sensitivity. Comparisons between the prone and supine images can be improved by registration between the scans. In this paper, we propose registering colon lumens, segmented from prone and supine images, using feature matching of the colon centerline and nonrigid registration of the lumen shapes represented as distance functions. Experimental registration results (n = 21 subjects) show a correspondence accuracy of 13.77 +/- 6.20 mm for a range of polyp sizes. The overlap in the registered lumen segmentations show an average Jaccard similarity coefficient of 0.915 +/- 0.07.

Intra-patient supine-prone colon registration in CT colonography using shape spectrum

CT colonography (CTC) is a minimally invasive screening technique for colorectal polyps and colon cancer. Since electronic colon cleansing (ECC) cannot completely remove the presence of pseudo-polyps, most CTC protocols acquire both prone and supine images to improve the visualization of the lumen wall and to reduce false positives. Comparisons between the prone and supine images can be facilitated by computerized registration between the scans. In this paper, we develop a fully automatic method for registering colon surfaces extracted from prone and supine images. The algorithm uses shape spectrum to extract the shape characteristics which are employed as the surface signature to find the correspondent regions between the prone and supine lumen surfaces. Our experimental results demonstrate an accuracy of 12.6 +/- 4.20 mm over 20 datasets. It also shows excellent potential in reducing the false positive when it is used to determine polyps through correspondences between prone and supine images.

On concise 3-D simple point characterizations: a marching cubes paradigm

The centerlines of tubular structures are useful for medical image visualization and computer-aided diagnosis applications. They can be effectively extracted by using a thinning algorithm that erodes an object layer by layer until only a skeleton is left. An object point is "simple" and can be safely deleted only if the resultant image is topologically equivalent to the original. Numerous characterizations of 3-D simple points based on digital topology already exist. However, little work has been done in the context of marching cubes (MC). This paper reviews several concise 3-D simple point characterizations in a MC paradigm. By using the Euler characteristic and a few newly observed properties in the context of connectivity-consistent MC, we present concise and more self-explanatory proofs. We also present an efficient method for computing the Euler characteristic locally for MC surfaces. Performance evaluations on different implementations are conducted on synthetic data and multidetector computed tomography examination of virtual colonoscopy and angiography.

Registration of prone and supine CT colonography scans based on correlation optimized warping and canonical correlation analysis

In this paper, we propose an automated method for colon registration from supine and prone scans. Four anatomical salient points on the colon are distinguished first. Then correlation optimized warping (COW) method is applied to the segments defined by the anatomical landmarks to find better global registration based on local correlation of segments. To utilize more features along the colon centerline, we extended the COW method by embedding canonical correlation analysis into it for correlation calculation of colon segments. To verify the effectiveness of the proposed method, we tested the algorithm on a CTC dataset of 19 patients with 23 polyps. Experimental results show that by using our method, the estimation error of polyp location could be reduced 68.5% (from 41.6mm to 13.1mm on average) compared to a traditional dynamic warping algorithm.

Comparison of polyp distance on CT colonography between supine and prone scans using an automated path-distance measurement tool: correlation with colonoscopy

OBJECTIVE

To investigate the proximity of polyp distance on CT colonography (CTC) between supine and prone scans using an automated path-distance measurement tool and to correlate the path distance of polyps with that of colonoscopy.

METHODS

Of 196 polyps in 85 patients, 98 polyps (25 <6 mm, 42 6-9.9 mm, 31 > or =10 mm) detected on supine and prone positions in 72 patients, were included. The location of each polyp, which was expressed as the distance from the tip of the rectum, was measured using an automated path-distance measurement tool and was compared between the two positions. The effect of colonic collapse on the proximity of polyp distance between the two scans was analyzed. The automated path distance of 50 polyps in the rectosigmoid colon was correlated with that of colonoscopy.

RESULTS

Mean difference of the automated path distances of polyps between the two positions was 2.6 cm and was not significantly different between the two positions (P > 0.05). Correlation coefficient (gamma) between the two positions was 0.9977. The difference of the distance of polyps between the groups with or without colonic collapse was not significant (P > 0.05). Automated path distance of 50 polyps in the sigmoid colon or rectum was generally well correlated with that on colonoscopy (gamma = 0.8005, P < 0.0001) and the mean difference was 5.1 cm. The mean difference increased as the polyp distance from the point of reference became further located, and reached significance when the distance was further than 30 cm from the tip of the rectum (P = 0.002).

CONCLUSION

Automated path distance of polyps matches closely between the two positions and is not influenced by the presence of collapsed segments. Polyps located 30 cm or further from the tip of the rectum on CTC do not match closely with that on colonoscopy.

Computer-aided detection for CT colonography: update 2007

Computed tomographic colonography (CTC) is an emerging technique for polyp detection in the colon. However, lesion detection can be challenging due to insufficient patient preparation, chosen CT technique or reader imperfection. The primary goal of computer-aided detection (CAD) for CTC is locating possible polyps, and presenting the reader with these polyp candidates. Other goals are sensitivity improvement and reduction of reading time and inter-observer variability. The multistep CAD procedure typically consists of segmentation of the colonic wall (e.g. region growing); selection of intermediate polyp candidates (curvature analysis, sphere fitting, normal analysis, slope density function ...); classification of final candidates for detection and listing suspicious polyps (location, size and volume). Remaining task for the radiologist is the validation or rejection of the polyp candidates. State-of-the-art CAD systems should require minimal or even no user interaction for the extraction of the colonic wall, offer a computation time less than 10-20 min and high sensitivity and specificity for different polyp sizes and shapes, with a low number of false positives. These systems have the potential to increase radiologist's performance and to decrease inter-reader variability. Besides CAD key techniques we also discuss new developments in CAD and describe recent applications facilitating CTC.

Deformable registration of prone and supine colons for CT colonography

CT colonography (CTC) is a non-invasive technique for detecting colorectal polyps and colon cancer. Through the addition of the prone scanning with the original supine scanning, the possibility of detecting the polyps is increased. The registration process for this application requires the comparison between the prone and supine colons for diagnosis. A level-set representation of the object boundary using a distance map is presented in this paper as an input to demons registration algorithm for supine and prone CT colonography image data. After first aligning the colon volumes based on the patient's anus position, distances inside and outside the objects' boundary are computed. The level-set from the distance map allows the demons algorithm to decide the moving direction for the initial demons' force between the two colons. We present a result with a 3 dimensional volume of a patient's colon. The results suggest that our method has excellent registration performance with high confidence even with considerable deformation of the colon lumen in 3 dimensional case.

Teniae coli-based circumferential localization system for CT colonography: feasibility study

This HIPAA-compliant study, with institutional review board approval and informed patient consent, was conducted to retrospectively develop a teniae coli-based circumferential localization method for guiding virtual colon navigation and colonic polyp registration. Colonic surfaces (n = 72) were depicted at computed tomographic (CT) colonography performed in 36 patients (26 men, 10 women; age range, 47-72 years) in the supine and prone positions. For 70 (97%) colonic surfaces, the tenia omentalis (TO), the most visible of the three teniae coli on a well-distended colonic surface, was manually extracted from the cecum to the descending colon. By virtually dissecting and flattening the colon along the TO, the authors developed a localization system involving 12 grid lines to estimate the circumferential positions of polyps. A sessile polyp would most likely (at 95% confidence level) be found within +/-1.2 grid lines (one grid line equals 1/12 the circumference) with use of the proposed method. By orienting and positioning the virtual cameras with use of the new localization system, synchronized prone and supine navigation was achieved. The teniae coli are extractable landmarks, and the teniae coli-based circumferential localization system helps guide virtual navigation and polyp registration at CT colonography.

CAD in CT colonography without and with oral contrast agents: progress and challenges

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.

Feasibility of automated matching of supine and prone CT-colonography examinations

Matching of prone and supine positions in CT colonography may improve accuracy of polyp detection. The purpose of this study was to investigate the feasibility of automatic prone-supine matching in CT-colonography using proven polyps as fixed points of reference. The method is based on similarities in the direction of centre-lines and allows for compression and extraction of the centre-lines in both positions. To illustrate the impact of the match error of the new method in practice, the visibility of the matched polyps in a primary three-dimensional unfolded cube setting was determined as well. The method was compared with a method that relies on the normalized distance along the centre-line (NDAC method). The median absolute match error was 14 mm (range 0-59 mm, average 20 mm) either proximal or distal from the actual polyp in prone position. In the observer study, 70% (26/37) of the polyps were directly visible in prone view. The overall difference in median absolute match error between both methods was small (2 mm), although half way along the centre-line there were polyps with substantial differences in match error (larger with NDAC). We concluded that automated prone-supine matching of CT-colonography studies is feasible and has a low match error. The difference with the NDAC method was small and not significant, although half way along the centre-line some differences were seen.

Computer-aided detection for virtual colonoscopy

Colon cancer is one of the leading causes of cancer deaths in the developed countries. Most colon cancers can be prevented if precursor colon polyps are detected and removed. Virtual colonoscopy, or CT colonography, has shown promise to be the future screening tool for polyp detection, with a number of studies performed at academic institutions showing high sensitivity and specificity. Two main factors limiting CT colonography in general use are its excessive interpretation time and the variable sensitivity among readers. This article discusses the potential of computer-aided detection to address these problems. We also review the current state of research in this field and the future roles and challenges of CAD for CT colonography.

Region-based supine-prone correspondence for the reduction of false-positive CAD polyp candidates in CT colonography

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.