High-performance computer aided detection system for polyp detection in CT colonography with fluid and fecal tagging

Abstract

CT colonography (CTC) is a feasible and minimally invasive method for the detection of colorectal polyps and cancer screening. Computer-aided detection (CAD) of polyps has improved consistency and sensitivity of virtual colonoscopy interpretation and reduced interpretation burden. A CAD system typically consists of four stages: (1) image preprocessing including colon segmentation; (2) initial detection generation; (3) feature selection; and (4) detection classification. In our experience, three existing problems limit the performance of our current CAD system. First, highdensity orally administered contrast agents in fecal-tagging CTC have scatter effects on neighboring tissues. The scattering manifests itself as an artificial elevation in the observed CT attenuation values of the neighboring tissues. This pseudo-enhancement phenomenon presents a problem for the application of computer-aided polyp detection, especially when polyps are submerged in the contrast agents. Second, general kernel approach for surface curvature computation in the second stage of our CAD system could yield erroneous results for thin structures such as small (6-9 mm) polyps and for touching structures such as polyps that lie on haustral folds. Those erroneous curvatures will reduce the sensitivity of polyp detection. The third problem is that more than 150 features are selected from each polyp candidate in the third stage of our CAD system. These high dimensional features make it difficult to learn a good decision boundary for detection classification and reduce the accuracy of predictions. Therefore, an improved CAD system for polyp detection in CTC data is proposed by introducing three new techniques. First, a scale-based scatter correction algorithm is applied to reduce pseudo-enhancement effects in the image pre-processing stage. Second, a cubic spline interpolation method is utilized to accurately estimate curvatures for initial detection generation. Third, a new dimensionality reduction classifier, diffusion map and local linear embedding (DMLLE), is developed for classification and false positives (FP) reduction. Performance of the improved CAD system is evaluated and compared with our existing CAD system (without applying those techniques) using CT scans of 1186 patients. These scans are divided into a training set and a test set. The sensitivity of the improved CAD system increased 18% on training data at a rate of 5 FPs per patient and 15% on test data at a rate of 5 FPs per patient. Our results indicated that the improved CAD system achieved significantly better performance on medium-sized colonic adenomas with higher sensitivity and lower FP rate in CTC.

A Robust and Fast System for CTC Computer-Aided Detection of Colorectal Lesions

“…Suzuki et al [21] extend Yoshida et al's approach [26] with a massive-training artificial neural network to further reduce false positive findings, showing promising results. Liu et al [23] apply manifold learning techniques such as diffusion maps and local linear embedding for dimensionality reduction in conjunction with a support vector machine. Recently in the computer vision and machine learning literature, there has been much interest in Adaboost, which forms a strong classifier from a set of weak learners and is less sensitive to overfitting than other classifiers.…”

SPIE Computer-Aided Diagnosis conference anniversary review

“…Automated colonic polyp detection, classification, and measurement of CTC with or without traditional cathartic colon cleansing were popular topics in the early years of the conference before CT colonography became a mainstream clinical technique. 64 – 69 Colon and colonic polyp analysis further included dual-energy CT colonography, taeniae coli detection, supine-prone colonic polyp registration, colitis detection, and colonoscopy video analysis. 70 – 74 Other abdominal topics have included bladder segmentation, small bowel analysis including segmentation and Crohn disease detection, endoscopic image analysis for polyps and cancers, liver organ and lesion segmentation, liver elastography, kidney segmentation, renal calculi detection, pancreas segmentation, pancreatic cyst classification, and uterine and placental segmentation.…”