Janne Näppi

Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps

We have developed a three-dimensional (3-D) computer-aided diagnosis scheme for automated detection of colonic polyps in computed tomography (CT) colonographic data sets, and assessed its performance based on colonoscopy as the gold standard. In this scheme, a thick region encompassing the entire colonic wall is extracted from an isotropic volume reconstructed from the CT images in CT colonography. Polyp candidates are detected by first computing of 3-D geometric features that characterize polyps, folds, and colonic walls at each voxel in the extracted colon, and then segmenting of connected components corresponding to suspicious regions by hysteresis thresholding based on these geometric features. We apply fuzzy clustering to these connected components to obtain the polyp candidates. False-positive (FP) detections are then reduced by computation of several 3-D volumetric features characterizing the internal structures of the polyp candidates, followed by the application of discriminant analysis to the feature space generated by these volumetric features. The locations of the polyps detected by our computerized method were compared to the gold standard of conventional colonoscopy. The performance was evaluated based on 43 clinical cases, including 12 polyps determined by colonoscopy. Our computerized scheme was shown to have the potential to detect polyps in CT colonography with a clinically acceptable high sensitivity and a low FP rate.

Feature-guided analysis for reduction of false positives in CAD of polyps for computed tomographic colonography.

We evaluated the effect of our novel technique of feature-guided analysis of polyps on the reduction of false-positive (FP) findings generated by our computer-aided diagnosis (CAD) scheme for the detection of polyps from computed tomography colonographic data sets. The detection performance obtained by use of feature-guided analysis in the segmentation and feature analysis of polyp candidates was compared with that obtained by use of our previously employed fuzzy clustering technique. We also evaluated the effect of a feature called modified gradient concentration (MGC) on the detection performance. A total of 144 data sets, representing prone and supine views of 72 patients that included 14 patients with 21 colorectal polyps 5-25 mm in diameter, were used in the evaluation. At a 100% by-patient (95% by-polyp) detection sensitivity, the FP rate of our CAD scheme with feature-guided analysis based on round-robin evaluation was 1.3 (1.5) FP detections per patient. This corresponds to a 70-75% reduction in the number of FPs obtained by use of fuzzy clustering at the same sensitivity levels. Application of the MGC feature instead of our previously used gradient concentration feature did not improve the detection result. The results indicate that feature-guided analysis is useful for achieving high sensitivity and a low FP rate in our CAD scheme.

Diagnostic accuracy of laxative-free computed tomographic colonography for detection of adenomatous polyps in asymptomatic adults, A prospective evaluation

BACKGROUND Colon screening by optical colonoscopy (OC) or computed tomographic colonography (CTC) requires a laxative bowel preparation, which inhibits screening participation. OBJECTIVE To assess the performance of detecting adenomas 6 mm or larger and patient experience of laxative-free, computer-aided CTC. DESIGN Prospective test comparison of laxative-free CTC and OC. The CTC included electronic cleansing and computer-aided detection. Optical colonoscopy examinations were initially blinded to CTC results, which were subsequently revealed during colonoscope withdrawal; this method permitted reexamination to resolve discrepant findings. Unblinded OC served as a reference standard. (ClinicalTrials.gov registration number: NCT01200303) SETTING Multicenter ambulatory imaging and endoscopy centers. PARTICIPANTS 605 adults aged 50 to 85 years at average to moderate risk for colon cancer. MEASUREMENTS Per-patient sensitivity and specificity of CTC and first-pass OC for detecting adenomas at thresholds of 10 mm or greater, 8 mm or greater, and 6 mm or greater; per-lesion sensitivity and survey data describing patient experience with preparations and examinations. RESULTS For adenomas 10 mm or larger, per-patient sensitivity of CTC was 0.91 (95% CI, 0.71 to 0.99) and specificity was 0.85 (CI, 0.82 to 0.88); sensitivity of OC was 0.95 (CI, 0.77 to 1.00) and specificity was 0.89 (CI, 0.86 to 0.91). Sensitivity of CTC was 0.70 (CI, 0.53 to 0.83) for adenomas 8 mm or larger and 0.59 (CI, 0.47 to 0.70) for those 6 mm or larger; sensitivity of OC for adenomas 8 mm or larger was 0.88 (CI, 0.73 to 0.96) and 0.76 (CI, 0.64 to 0.85) for those 6 mm or larger. The specificity of OC at the threshold of 8 mm or larger was 0.91 and at 6 mm or larger was 0.94. Specificity for OC was greater than that for CTC, which was 0.86 at the threshold of 8 mm or larger and 0.88 at 6 mm or larger (P= 0.02). Reported participant experience for comfort and difficulty of examination preparation was better with CTC than OC. LIMITATIONS There were 3 CTC readers. The survey instrument was not independently validated. CONCLUSION Computed tomographic colonography was accurate in detecting adenomas 10 mm or larger but less so for smaller lesions. Patient experience was better with laxative-free CTC. These results suggest a possible role for laxative-free CTC as an alternate screening method.

Automated knowledge-guided segmentation of colonic walls for computerized detection of polyps in CT colonography

Purpose We have developed a novel automated technique for segmenting colonic walls for the application of computer-aided polyp detection in CT colonography. In particular, the technique was designed to minimize the presence of extracolonic components, such as small bowel, in the segmented colon. Methods The segmentation technique combines an improved version of our previously reported anatomy-oriented colon segmentation technique with a colon-based analysis step that performs self-adjusting volume-growing within the colonic lumen. Extracolonic components are eliminated by intersecting of the resulting two segmentations, so that the colonic walls remain in the intersection. The technique was evaluated on 88 CT colonography datasets. The colon segmentations were evaluated subjectively by four radiologists, as well as objectively by performance of an automated polyp detection on the segmentation. For comparison, the tests were also performed for the anatomy-oriented colon segmentation technique. Results On average, the technique covered 98% of the visible colonic walls. Approximately 50% of the extracolonic components remaining in the anatomy-oriented segmentation were removed, but 10–15% of the segmentation still contained extracolonic components. The dataset-based false-positive rate of the automated polyp detection was improved by 10% without compromising the 100% case-based sensitivity, and the case-based false-positive rate was improved by 15% over the previous false-positive rate. Conclusions The technique segments practically all of the colonic walls in the region of diagnostic quality with a large reduction in the amount of extracolonic components over our previously used technique. The new segmentation improves the specificity of our computer-aided polyp detection scheme significantly without any degradation in detection sensitivity.

Computerized detection of colorectal masses in CT colonography based on fuzzy merging and wall-thickening analysis.

In recent years, several computer-aided detection (CAD) schemes have been developed for the detection of polyps in CT colonography (CTC). However, few studies have addressed the problem of computerized detection of colorectal masses in CTC. This is mostly because masses are considered to be well visualized by a radiologist because of their size and invasiveness. Nevertheless, the automated detection of masses would naturally complement the automated detection of polyps in CTC and would produce a more comprehensive computer aid to radiologists. Therefore, in this study, we identified some of the problems involved with the computerized detection of masses, and we developed a scheme for the computerized detection of masses that can be integrated into a CAD scheme for the detection of polyps. The performance of the mass detection scheme was evaluated by application to clinical CTC data sets. CTC was performed on 82 patients with helical CT scanners and reconstruction intervals of 1.0-5.0 mm in the supine and prone positions. Fourteen patients (17%) had a total of 14 masses of 30-50 mm, and sixteen patients (20%) had a total of 30 polyps 5-25 mm in diameter. Four patients had both polyps and masses. Fifty-six of the patients (68%) were normal. The CTC data were interpolated linearly to yield isotropic data sets, and the colon was extracted by use of a knowledge-guided segmentation technique. Two methods, fuzzy merging and wall-thickening analysis, were developed for the detection of masses. The fuzzy merging method detected masses with a significant intraluminal component by separating the initial CAD detections of locally cap-like shapes within the colonic wall into mass candidates and polyp candidates. The wall-thickening analysis detected nonintraluminal masses by searching the colonic wall for abnormal thickening. The final regions of the mass candidates were extracted by use of a level set method based on a fast marching algorithm. False-positive (FP) detections were reduced by a quadratic discriminant classifier. The performance of the scheme was evaluated by use of a leave-one-out (round-robin) method with by-patient elimination. All but one of the 14 masses, which was partially cut off from the CTC data set in both supine and prone positions, were detected. The fuzzy merging method detected 11 of the masses, and the wall-thickening analysis detected 3 of the masses including all nonintraluminal masses. In combination, the two methods detected 13 of the 14 masses with 0.21 FPs per patient on average based on the leave-one-out evaluation. Most FPs were generated by extrinsic compression of the colonic wall that would be recognized easily and quickly by a radiologist. The mass detection methods did not affect the result of the polyp detection. The results indicate that the scheme is potentially useful in providing a high-performance CAD scheme for the detection of colorectal neoplasms in CTC.

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

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

Centerline‐based colon segmentation for CT colonography

We have developed a fully automated algorithm for colon segmentation, centerline-based segmentation (CBS), which is faster than any of the previously presented segmentation algorithms, but also has high sensitivity as well as high specificity. The algorithm first thresholds a set of unprocessed CT slices. Outer air is removed, after which a bounding box is computed. A centerline is computed for all remaining regions in the thresholded volume, disregarding segments related to extracolonic structures. Centerline segments are connected, after which the anatomy-based removal of segments representing extracolonic structures occurs. Segments related to the remaining centerline are locally region grown, and the colonic wall is found by dilation. Shape-based interpolation provides an isotropic mask. For 38 CT datasets, CBS was compared with the knowledge-guided segmentation (KGS) algorithm for sensitivity and specificity. With use of a 1.5 GHz AMD Athlon-based PC, the average computation time for the segmentation was 14.8 s. The sensitivity was, on average, 96%, and the specificity was 99%. A total of 21% of the voxels segmented by KGS, of which 96% represented extracolonic structures and 4% represented the colon, were removed.

Comparative evaluation of the fecal-tagging quality in CT colonography: barium vs. iodinated oral contrast agent.

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

Virtual endoscopic visualization of the colon by shape-scale signatures

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

Fast and robust computation of colon centerline in CT colonography

Although several methods for generating the centerline of a colon from CT colonographic scans have been proposed, in general they are time-consuming and do not take into account that the images of the colon may be of nonoptimal quality, with collapsed regions, and stool within the colon. Furthermore, the colonic lumen or wall, which is often used as a basis for computation of a centerline, is not always precisely segmented. In this study, we have developed an algorithm for computation of a colon centerline that is fast compared to the centerline algorithms presented in the reviewed literature, and that relies little on a complete colon segments identification. The proposed algorithm first extracts local maxima in a distance map of a segmented colonic lumen. The maxima are considered to be nodes in a set of graphs, and are iteratively linked together, based on a set of connection criteria, giving a minimum distance spanning tree. The connection criteria are computed from the distance from object boundary, the Euclidean distance between nodes and the voxel values on the pathway between pairs of nodes. After the last iteration, redundant branches are removed and end segments are recovered for each remaining graph. A subset of the initial maxima is used for distinguishing between the colon and noncolonic centerline segments among the set of graphs, giving the final centerline representation. A phantom study showed that, with respect to phantom variations, the algorithm achieved nearly constant computation time (2.3-2.9 s) except for the most extreme setting (20.2 s). The algorithm successfully found all, or most of, the centerline (93% - 100%). Displacement from optimum varied with colon diameter (1.2-6.6 mm). By use of 40 CT colonographic scans, the computer-generated centerlines were compared with the centerlines generated by three radiologists. The similarity was measured based on percent coverage and average displacement. The computer-generated centerlines, when compared with human-generated centerlines, had approximately the same displacement as when the human-generated centerlines were compared among each other (3.8 mm versus 4.0 mm). The coverage of the computer-generated centerlines was slightly less than that of the human-generated centerlines (92% versus 94%). The 40 centerlines were, on average, computed in 10.5 seconds, including computation time for the distance transform, with an Intel Pentium-based 800 MHz computer, as compared with 12-17 seconds or more (excluding computation time for the distance transform needed) per centerline as reported in other studies.