A Model-Driven Bayesian Method for Polyp Detection and False Positive Suppression in CT Colonography Computer-Aided Detection

A Model-Driven Bayesian Method for Polyp Detection and False Positive Suppression in CT Colonography Computer-Aided Detection

Xujiong Ye (Medicsight PLC, UK) and Greg Slabaugh (Medicsight PLC, UK)
DOI: 10.4018/978-1-4666-0059-1.ch011
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This chapter presents an automated method to identify colonic polyps and suppress false positives for Computer-Aided Detection (CAD) in CT Colonography (CTC). The method formulates the problem of polyp detection as a probability calculation through a unified Bayesian statistical approach. The polyp likelihood is modeled with a combination of shape, intensity, and location features, while also taking into account the spatial prior probability encoded by a Markov Random Field. A second principal curvature PDE provides a shape model; and partial volume effect is incorporated in the intensity model. When evaluated on a large multi-center dataset of colonic CT scans, the CAD detection performance as well as the volume overlap ratio demonstrate the potential of the proposed method. The method results in an average 24% reduction of false positives with no impact on sensitivity. The method is also applicable to generation of initial candidates for CTC CAD with high detection sensitivity and relatively lower false positives, compared to other non-Bayesian methods.
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1. Introduction

Colorectal Cancer (CRC) is the second leading cause of cancer related death in western countries. Early detection and removal of polyps has been associated with reduction in the incidence of colorectal cancer (Winawer, et al., 2003). As a new minimally-invasive screening technique, Computed Tomography (CT) Colonoscopy (CTC), also more popularly known as Virtual Colonoscopy (VC), uses CT imaging and dedicated interactive three-dimensional (3D) and two-dimensional (2D) imaging software to evaluate the colon. CTC has shown several advantages over the traditional Optical Colonoscopy (OC) for screening (Pickhardt, et al., 2003). Clinical studies suggest CTC can provide similar detection performance as colonoscopy but has a reduced risk of complication (Kim, et al., 2007). The CT scan is performed in supine and prone positions during a breath-hold acquisition. No sedation or analgesics are required.

Although CTC has been demonstrated to be an effective alternative colorectal screening approach (Johnson, et al., 2008), the manual interpretation of the CT data sets is very time-consuming due to the large quantity of data generated (typically 800-2000 images per patient) and some key factors (such as reader experience and specific skills) can affect the quality of CTC interpretation. Computer-Aided Detection (CAD) for CTC has been developed for the automated detection of polyps in order to overcome the difficulties of manual CTC interpretation. CAD offers the radiologist a second opinion, and has been shown to reduce the variability of the procedure. The clinical impact of CAD is being investigated. CTC CAD has been suggested as an effective second reader and may enhance the efficacy of CTC examinations through increased sensitivity of CTC examinations (Lawrence, et al., 2010).

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