Comparative performance of a primary-reader and second-reader paradigm of computer-aided detection for CT colonography in a low-prevalence screening population.

Objective: To compare the efficacy of computer-aided detection (CAD) for computed tomographic colonography (CTC) when employed as either primary-reader or second-reader paradigms in a low-prevalence screening population.

Methods: Ninety screening patients underwent same-day CTC and colonoscopy. Four readers prospectively interpreted all CTC data sets using a second-reader paradigm (unassisted interpretation followed immediately by CAD assistance).

Incremental benefit of computer-aided detection when used as a second and concurrent reader of CT colonographic data: multiobserver study.

Purpose: To quantify the changes in reader performance levels, if any, during interpretation of computed tomographic (CT) colonographic data when a computer-aided detection (CAD) system is used as a second or concurrent reader.

Materials And Methods: After institutional review board approval was obtained, 16 experienced radiologists searched for polyps in 112 patients, 56 of whom had 132 polyps. Each case was interpreted on three separate occasions by using an unassisted (without CAD), second-read CAD, or concurrent CAD reading paradigm.

Automatic Graph Cut Segmentation of Lesions in CT Using Mean Shift Superpixels.

This paper presents a new, automatic method of accurately extracting lesions from CT data. It first determines, at each voxel, a five-dimensional (5D) feature vector that contains intensity, shape index, and 3D spatial location. Then, nonparametric mean shift clustering forms superpixels from these 5D features, resulting in an oversegmentation of the image.

Shape-based computer-aided detection of lung nodules in thoracic CT images.

In this paper, a new computer tomography (CT) lung nodule computer-aided detection (CAD) method is proposed for detecting both solid nodules and ground-glass opacity (GGO) nodules (part solid and nonsolid). This method consists of several steps. First, the lung region is segmented from the CT data using a fuzzy thresholding method.

Computer-aided detection in computed tomography colonography: current status and problems with detection of early colorectal cancer.

Purpose: The aim of this study was to evaluate the usefulness of computer-aided detection (CAD) in diagnosing early colorectal cancer using computed tomography colonography (CTC).

Materials And Methods: A total of 30 CTC data sets for 30 early colorectal cancers in 30 patients were retrospectively reviewed by three radiologists. After primary evaluation, a second reading was performed using CAD findings.

Flat neoplasia of the colon: CT colonography with CAD.

Attention is focusing on testing and developing computer aided detection (CAD) systems to reliably highlight flat polyps and cancers to the reporting radiologist during CT colonography. This review will discuss the clinical relevance of flat colonic neoplasia, describe some of the challenges facing CAD detection algorithms, and review the current CAD literature on this topic.

Efficient computer-aided detection of ground-glass opacity nodules in thoracic CT images.

In this paper, an efficient compute-aided detection method is proposed for detecting Ground-Glass Opacity (GGO) nodules in thoracic CT images. GGOs represent a clinically important type of lung nodule which are ignored by many existing CAD systems. Anti-geometric diffusion is used as preprocessing to remove image noise.

Reduction of false positives in polyp detection using weighted support vector machines.

Colorectal cancer is the third highest cause of cancer deaths in US (2007). Early detection and treatment of colon cancer can significantly improve patient prognosis. Manual identification of polyps by radiologists using CT Colonography can be labour intensive due to the increasing size of datasets and is error prone due to the complexity of the anatomical structures.