Automatic Polyp Detection Research Articles

Comparative Performance of Two Polyp Detection Systems on CT Colonography

The purpose of our study was to evaluate two current automatic polyp detection systems to determine their sensitivity and false-positive rate in patients who have undergone CT colonography and subsequent endoscopy. We evaluated two polyp detection systems--Polyp Enhanced Viewing (PEV) and the Summers computer-aided detection (CAD) system (National Institutes of Health [NIH]) using a unique cohort of CT colonography examinations: 31 examinations with true-positive lesions identified by radiologists and 34 examinations with false-positive lesions incorrectly identified by radiologists. All patients had reference-standard colonoscopy within 7 days of CT. Candidate lesions were compared with the endoscopic reference standard and prospective radiologist interpretation. The sensitivity and false-positive rates were calculated for each system. The NIH system had a higher sensitivity than the PEV tool for polyps > or = 1 cm (22/23, 96%; 78-99%, 95% CI vs 14/23, 61%; 38-81%, 95% CI; p = 0.008, respectively). There was no significant difference in the detection of medium-sized polyps 6-9 mm in size (8/13 vs 6/13, p = 0.68, respectively). The PEV tool had an average of 1.18 false-positive detections per patient, whereas the NIH tool had an average of 5.20 false-positive detections per patient, with the PEV tool having significantly fewer false-positive detections in both patient groups (p < 0.001). One polyp detection system tended to operate with a higher sensitivity, whereas the other tended to operate with a lower false-positive rate. Prospective trials using polyp detection systems as a primary or secondary means of CT colonography interpretation appear warranted.

Development of a synthetic phantom for the selection of optimal scanning parameters in CAD–CT colonography

The aim of this paper is to present the development of a synthetic phantom that can be used for the selection of optimal scanning parameters in computed tomography (CT) colonography. In this paper we attempt to evaluate the influence of the main scanning parameters including slice thickness, reconstruction interval, field of view, table speed and radiation dose on the overall performance of a computer aided detection (CAD)–CTC system. From these parameters the radiation dose received a special attention, as the major problem associated with CTC is the patient exposure to significant levels of ionising radiation. To examine the influence of the scanning parameters we performed 51 CT scans where the spread of scanning parameters was divided into seven different protocols. A large number of experimental tests were performed and the results analysed. The results show that automatic polyp detection is feasible even in cases when the CAD– CTC system was applied to low dose CT data acquired with the following protocol: 13 mAs/rotation with collimation of 1.5 mm × 16 mm, slice thickness of 3.0 mm, reconstruction interval of 1.5 mm, table speed of 30 mm per rotation. The CT phantom data acquired using this protocol was analysed by an automated CAD–CTC system and the experimental results indicate that our system identified all clinically significant polyps (i.e. larger than 5 mm).

Lines of Curvature for Polyp Detection in Virtual Colonoscopy

Computer-aided diagnosis (CAD) is a helpful addition to laborious visual inspection for preselection of suspected colonic polyps in virtual colonoscopy. Most of the previous work on automatic polyp detection makes use of indicators based on the scalar curvature of the colon wall and can result in many false-positive detections. Our work tries to reduce the number of false-positive detections in the preselection of polyp candidates. Polyp surface shape can be characterized and visualized using lines of curvature. In this paper, we describe techniques for generating and rendering lines of curvature on surfaces and we show that these lines can be used as part of a polyp detection approach. We have adapted existing approaches on explicit triangular surface meshes, and developed a new algorithm on implicit surfaces embedded in 3D volume data. The visualization of shaded colonic surfaces can be enhanced by rendering the derived lines of curvature on these surfaces. Features strongly correlated with true-positive detections were calculated on lines of curvature and used for the polyp candidate selection. We studied the performance of these features on 5 data sets that included 331 pre-detected candidates, of which 50 sites were true polyps. The winding angle had a significant discriminating power for true-positive detections, which was demonstrated by a Wilcoxon rank sum test with p < 0.001. The median winding angle and inter-quartile range (IQR) for true polyps were 7.817 and 6.770 - 9.288 compared to 2.954 and 1.995 - 3.749 for false-positive detections.

Computer-Aided Detection of Colonic Polyps Using Low-Dose CT Acquisitions

This report proposes an alternative method for the automatic detection of colonic polyps that is robust enough to be directly applicable on low-dose computed tomographic data. The polyp modeling process takes into account both the gray-level appearance of polyps (intensity profiles) and their geometry (extended Gaussian images). Spherical harmonic decompositions are used for comparison purposes, allowing fast estimation of the similarity between a candidate and a set of previously computed models. Starting from the original raw data (acquired at 55 mA), five patient data sets (prone and supine scans) are reconstructed at different dose levels (to 5 mA) by using different kernel filters, slice overlaps, and increments. Additionally, the efficacy of applying an edge-preserving smoothing filter before detection is assessed. Although image quality decreases when decreasing acquisition milliamperes, all polyps greater than 6 mm are detected successfully, even at 15 mA. Although not important at high doses, smoothing improves detection results for ultra-low-dose (tube current<15 mA) data. The advantage of low-dose scans is a significant decrease in effective dose from 4.93 to 1.61 mSv while retaining high detection values, particularly important when thinking of population screening.

Hybrid segmentation of colon filled with air and opacified fluid for CT colonography

Reliable segmentation of the colon is a requirement for three-dimensional visualization programs and automatic detection of polyps on computed tomography (CT) colonography. There is an evolving clinical consensus that giving patients positive oral contrast to tag out remnants of stool and residual fluids is mandatory. The presence of positive oral contrast in the colon adds an additional challenge for colonic segmentation but ultimately is beneficial to the patient because the enhanced fluid helps reveal polyps in otherwise hidden areas. Therefore, we developed a new segmentation procedure which can handle both air- and fluid-filled parts of the colon. The procedure organizes individual air- and fluid-filled regions into a graph that enables identification and removal of undesired leakage outside the colon. In addition, the procedure provides a risk assessment of possible leakage to assist the user prior to the tedious task of visual verification. The proposed hybrid algorithm uses modified region growing, fuzzy connectedness and level set segmentation. We tested our algorithm on 160 CT colonography scans containing 183 known polyps. All 183 polyps were in segmented regions. In addition, visual inspection of 24 CT colonography scans demonstrated good performance of our procedure: the reconstructed colonic wall appeared smooth even at the interface between air and fluid and there were no leaked regions.

Robust automated polyp detection for low-dose and normal-dose virtual colonoscopy

We evaluated the performance of an automated polyp-detection scheme on both low-dose and normal-dose virtual colonoscopy data from different institutions. The polyp-detection algorithm is statistically trained on low-dose virtual colonoscopy data from 13 patients with a predicted sensitivity of 92% and a false-positive rate of 9 objects per study. An independent test on data from 50 patients with similar preparation and acquisition as the training data and 32 patients having completely different preparation and acquisition confirms the prediction. Our results show that it is possible to design a sensitive and specific polyp-detector that is robust for low-dose and normal-dose, isotropic and anisotropic, for faecal-tagged and non-tagged CT colonoscopy data.

Computer-Aided Detection of Colorectal Polyps at CT Colonography

colonography (CTC) research. We blindly evaluated two techniques for automated polyp detection using common test data.

Invited Commentary to: 'Virtual Dissection and Automated Polyp Detection of the Colon Based on Spiral CT - Techniques and Prelim inary Experience on a Cadaveric Phantom' (Eur. Surg. 2002;34:143 - 149)

Invited Commentary to: 'Virtual Dissection and Automated Polyp Detection of the Colon Based on Spiral CT - Techniques and Prelim inary Experience on a Cadaveric Phantom' (Eur. Surg. 2002;34:143 - 149)

Invited Commentary to: 'Virtual Dissection and Automated Polyp Detection of the Colon Based on Spiral CT - Techniques and Preliminary Experience on a Cadaveric Phantom' (Eur. Surg. 2002;34:143 - 149)

Invited Commentary to: 'Virtual Dissection and Automated Polyp Detection of the Colon Based on Spiral CT - Techniques and Preliminary Experience on a Cadaveric Phantom' (Eur. Surg. 2002;34:143 - 149)

Virtual Dissection and Automated Polyp Detection of the Colon Based on Spiral CT - Techniques and Preliminary Experience on a Cadaveric Phantom

Background: CT colonography was found to be sensitive and specific for detection of colonic polyps and colorectal cancer (CRC). Depending on the software used, CT colonography requires a certain amount of operator interaction, which limits it’s widespread usage. The goal of this papers is to present two novel automated techniques for displaying CT colonography: virtual dissection and automated colonic polyp detection.

Automated polyp detection at CT colonography: feasibility assessment in a human population.

To test the feasibility of and improve a computer algorithm to automatically detect colonic polyps in real human computed tomographic (CT) colonographic data sets. Twenty patients with known polyps underwent CT colonography in the supine position. CT colonographic data were processed by using a shape-based algorithm that depicts masses that protrude into the lumen. We studied nine shape criteria and three isosurface threshold settings. Results were compared with those of conventional colonoscopy performed the same day. There were 50 polyps (28 were > or =10 mm in size; 12, 5-9 mm; 10, <5 mm). The sensitivity with optimal settings for detecting polyps 10 mm or greater was 64% (18 of 28). Sensitivity improved to 71% (10 of 14) for polyps 10 mm or greater in well-distended colonic segments. Performance decreased for polyps less than 10 mm, poorly distended colonic segments, and other shape algorithms. There was a mean of six false-positive lesion sites per colon. These sites were reduced 39% to 3.5 per colon by sampling CT attenuation at the lesion site and discarding sites having attenuation less than a threshold. Automated detection of colonic polyps, especially clinically important large polyps, is feasible. Colonic distention is an important determinant of sensitivity. Further increases in sensitivity may be achieved by adding prone CT colonography.

Automated polyp detector for CT colonography: feasibility study.

An abdominal computed tomographic scan was modified by inserting 10 simulated colonic polyps with use of methods that closely mimic the attenuation, noise, and polyp-colon wall interface of naturally occurring polyps. A shape-based polyp detector successfully located six of the 10 polyps. When settings that enhanced the edge profile of polyps were chosen, eight of 10 polyps were detected. There were no false-positive detections. Shape analysis is technically feasible and is a promising approach to automated polyp detection.

Enhanced virtual colonoscopy system employing automatic detection of colon polyps

Enhanced virtual colonoscopy system employing automatic detection of colon polyps