Efficient computerized polyp detection for CT colonography.

COLORECTAL CARCINOMA is the leading cause of cancer-related death in the nonsmoking United States population, with about 60,000 annual fatalities.1 Yet it is the most preventable cancer if the cancer precursor, polyps, can be detected and removed at an early stage. The current gold standard for colorectal cancer screening is optical colonoscopy because of its high accuracy compared to other screening methods such as fecal occult blood test, double-contrast barium enema, and sigmoidoscopy. Increased optical colonoscopy compliance for subjects over 50 would substantially decrease the cancer mortality; however, fewer than 40% of the average risk adults reported having received a optical colonoscopy in the past 5 years.1 Optical colonoscopy compliance is low due to its discomfort, perforation risk, high cost, and high negative statistic. Computed tomographic colonography (CTC), or virtual colonoscopy, has been considered a promising replacement for optical colonoscopy as a regular screening method since its introduction in 19942 and has been an active research field for both clinical researchers and technology developers. On the clinical side, many medical centers perform a CTC procedure, and some of them are doing large population clinical experiments on CTC. A multi-center clinical trial3 of CTC over a large asymptomatic population (1233 subjects) published encouraging results, which show the viability of virtual colonoscopy as a screening tool. Regarding radiologist interpretation, CTC acceptance has been hindered by two issues, detection accuracy, which is generally unacceptable for clinical practice, and interobserver variances. 4, 6, 7With observers at diverse levels of expertise and using different reading methods, multiple centers reported radiologists’ CTC results with sensitivities between 20% and 90% for 6-mm and larger polyps.5 Aside from interpretation variation, detection performance is affected by different raw image quality due to patient preparation, scanner parameters, and dose consideration. On the technology side, in addition to CT scanner improvement from single to multi-row detectors, researchers have made efforts to use computer techniques to solve the above clinical problems. Volume rendering and visualization that are available on commercial systems directly affect radiologists’ results.8 Computer-aided polyp detection (CAPD) or computerized polyp detection (CPD) may improve the platform to address the above issues in clinical research. Yoshida and Nappi reported a fully automatic CPD system tested on 43 patients with 12 polyps and yielded 1 to 2 false positive results (FPs)/case.9 Nappi and yoshida have extended the algorithm by introducing another geometric feature, modified gradient concentration, and applied the system to more patients but with no substantial improvement.10 Gokturk et al reported their statistical ROSS method used in false positive reduction,(11,12) which produced a 40-50% increment of specificity without sensitivity loss, an encouraging result but still far from radiologist accuracy, especially for the very low specificity. The main difficulties and challenges of CAPD in CTC are summarized by Summers.13 Given the CTC raw data, segmentation is the first step in our CPD scheme. There are two major segmentation approaches, surface methods and volume methods. In surface methods, a mesh surface that represents the colon lumen needs to be segmented from the volume data.14-16 In volume methods, the lumen surface voxels and/or their neighboring voxels are segmented using gradient operators(9, 10, 17) and volume subtraction.9 There is no convincing proof for the advantage of either method. For the subsequent polyp detection, there are also two sources for feature calculation. They are: Local volumetric information calculated from the intensity values of a small volumetric neighborhood. Local shape information calculated from the mesh surface structure. The local volumetric information approach uses the intensities of voxels in a neighborhood and the extracted features computed from them so that a CPD algorithm can classify the neighborhood. For example, Yoshida and Nappi.9 defined the local shape index (SI), curvedness (CV), and directional gradient concentration (DCG) as three geometric features in their classifier. Statistical values based on voxel intensities may provide useful features. When local shape information from the mesh surface is used, curvature-based shape features can be calculated from a group of connected vertices or from the polyp candidates. Also the size, roundness, etc., can be obtained easily from the surface data. Furthermore, the surface method intrinsically reduces the noise effect in curvature calculation. Making the decision to use one or both of the sources to calculate the geometric features is the beginning of feature selection and reduction. Selection is accomplished by observation, expert opinion, taking all possible measurements, and/or any other methods that transform knowledge of the system into metrics. Feature reduction is the subsequent and optional procedure to find the most useful and most independent set of features, from the originals, that also produces the optimum classification. In many practical situations, feature reduction is accomplished by observation or evaluating the system performance with a framing database. The goal of classifier selection is to find the best classifier for partitioning the feature space. Bayes classifiers18 are well-studied and widely used in many medical applications. A hyperplane, corresponding to a linear discriminant function (LDF), and a second-order surface, corresponding to a quadratic discriminant function (QDF), are the most frequently used Bayes classifiers because of their simplicity and effectiveness. Support vector machines (SVM) transform the given feature space to higher dimensions so that the sample vectors are linearly separable in the transformed space.11 In the above classifiers, we often assume(1) normal distributions for both the true positive and false positive, or non-polyp categories, and (2) sufficient training samples for parameter estimation. However, these assumptions are not quite practical in polyp detection. The first reason is that the false positive category is the dominant class, which may bias the calculation of the average covariance matrices. The second reason is that the normal distribution assumption for the false positive category is questionable because the category embraces anything other than polyps and the occurrences of the feature vectors of false positives in the feature space are generally unpredictable. Therefore, we propose an MLDF method, which corresponds to several empirical hyperplanes that frame a hypercubic region in a given feature space, and a MAP method, which requires parametric training only for the polyp category. We have developed a fully automatic CPD system, which begins with raw DICOM data and ends with a list of detected polyps by employing a typical CAPD/CPD scheme: segmentation, polyp candidate generation, and false positive reduction. In our system, both supine and prone scans are combined to increase the colon coverage in computerized segmentation14 and in turn to improve the detection sensitivity. Colon deformation between the two positions makes the surface registration practically unsolvable. Fortunately, the clinical significance of patient-based accuracy of CTC renders registration unnecessary; if a polyp showed in either or both scans, the patient would be forwarded to optical colonoscopy. In this specific situation, we redefine the accuracy terms for the CPD when using both scans. A true positive occurs when an optical colonoscopy polyp finding is CPD detected in either or both scans; a false positive occurs when a CPD finding in any scan cannot be matched to any of the optical colonoscopy polyp findings. Our results demonstrate that using both scans improves the detection sensitivity. The segmentation algorithm is presented in the accompanying article.14 In this article, we focus on the subsequent polyp detection. The details of our detection algorithm as well as a brief introduction of segmentation are presented below in Methodology, while the section titled Data describes the scanner parameters and the procedure for data acquisition. A description of the detection results follows; and we close with the Discussion and Conclusions.