A Pipeline for Computer Aided Polyp Detection

Hong, Wei; Qiu, Feng; Kaufman, Arie

doi:10.1109/tvcg.2006.112

Cited by 50 publications

(37 citation statements)

References 26 publications

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“…Specifically, texture based approaches might improve the detection of flat lesions, e.g. Wan et al [40] and Hong et al [41]. Other sources of FN were 10-mm sessile lesions that were hard/impossible to find by a radiologist.…”

Section: Discussionmentioning

confidence: 99%

Automated Detection and Segmentation of Large Lesions in CT Colonography

Grigorescu

Nevo

Liedenbaum³

et al. 2010

IEEE Trans. Biomed. Eng.

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Abstract-Computerized tomographic colonography is a minimally invasive technique for the detection of colorectal polyps and carcinoma. Computer-aided diagnosis (CAD) schemes are designed to help radiologists locating colorectal lesions in an efficient and accurate manner. Large lesions are often initially detected as multiple small objects, due to which such lesions may be missed or misclassified by CAD systems. We propose a novel method for automated detection and segmentation of all large lesions, i.e., large polyps as well as carcinoma. Our detection algorithm is incorporated in a classical CAD system. Candidate detection comprises preselection based on a local measure for protrusion and clustering based on geodesic distance. The generated clusters are further segmented and analyzed. The segmentation algorithm is a thresholding operation in which the threshold is adaptively selected. The segmentation provides a size measurement that is used to compute the likelihood of a cluster to be a large lesion. The large lesion detection algorithm was evaluated on data from 35 patients having 41 large lesions (19 of which malignant) confirmed by optical colonoscopy. At five false positive (FP) per scan, the classical system achieved a sensitivity of 78%, while the system augmented with the large lesion detector achieved 83% sensitivity. For malignant lesions, the performance at five FP/scan was increased from 79% to 95%. The good results on malignant lesions demonstrate that the proposed algorithm may provide relevant additional information for the clinical decision process.

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Section: Discussionmentioning

confidence: 99%

Automated Detection and Segmentation of Large Lesions in CT Colonography

Grigorescu

Nevo

Liedenbaum³

et al. 2010

IEEE Trans. Biomed. Eng.

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“…Colon flattening techniques have been proposed using conformal mapping [11] and holomorphic 1-form parameterization [13]. The conformally flattened colon was used in the detection of colonic polyps [14] and supineprone colon registration [44]. Further, surface parameterization using harmonic functions has also been used in graphics [37] and medical imaging of the brain [34] and blood vessels [46].…”

Section: Related Workmentioning

confidence: 99%

Colon Flattening Using Heat Diffusion Riemannian Metric

Gurijala

Zeng

et al. 2013

IEEE Trans. Visual. Comput. Graphics

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We propose a new colon flattening algorithm that is efficient, shape-preserving, and robust to topological noise. Unlike previous approaches, which require a mandatory topological denoising to remove fake handles, our algorithm directly flattens the colon surface without any denoising. In our method, we replace the original Euclidean metric of the colon surface with a heat diffusion metric that is insensitive to topological noise. Using this heat diffusion metric, we then solve a Laplacian equation followed by an integration step to compute the final flattening. We demonstrate that our method is shape-preserving and the shape of the polyps are well preserved. The flattened colon also provides an efficient way to enhance the navigation and inspection in virtual colonoscopy. We further show how the existing colon registration pipeline is made more robust by using our colon flattening. We have tested our method on several colon wall surfaces and the experimental results demonstrate the robustness and the efficiency of our method.

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“…They reach 100% sensitivity with a relative low false positives (FP) rate, using heuristic thresholds and texture features. Hong et al, [18], map the 3D surface to a rectangle, use 2D clustering, and reduce false positives with shape and texture features. Sundaram et al, [19], compute curvatures via the Smoothed Shape Operators method, and use principal curvatures and Gaussian curvatures to detect polyps.…”

Section: A Virtual Colonoscopy Cad Reviewmentioning

confidence: 99%

A Complete System for Polyps Flagging in Virtual Colonoscopy

Fiori¹,

Musé²,

Sapiro³

2011

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Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. Computer tomographic colonography, combined with computer-aided detection, is a promising emerging technique for colonic polyp analysis. We present a complete pipeline for polyp flagging based on a simple segmentation technique that enhances polyps, a multi-scale candidate polyp delineation and new texture and geometric features that consider both the information in the candidate polyp location and its immediate surrounding area. The proposed algorithm is tested with ground truth data, including flat and small polyps which are hard to detect even with optical colonoscopy, obtaining very promising results. For polyps greater than 6mm in size we achieve 100% sensitivity with just 0:8 false positives per study, and for polyps greater than 3mm in size we achieve 100% sensitivity with 2:2 false positives per study. Abstract-Computer tomographic colonography, combined with computer-aided detection, is a promising emerging technique for colonic polyp analysis. We present a complete pipeline for polyp flagging based on a simple segmentation technique that enhances polyps, a multi-scale candidate polyp delineation, and new texture and geometric features that consider both the information in the candidate polyp location and its immediate surrounding area. The proposed algorithm is tested with ground truth data, including flat and small polyps which are hard to detect even with optical colonoscopy, obtaining very promising results. For polyps greater than 6mm in size we achieve 100% sensitivity with just 0.8 false positives per study, and for polyps greater than 3mm in size we achieve 100% sensitivity with 2.2 false positives per study.

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A Pipeline for Computer Aided Polyp Detection

Cited by 50 publications

References 26 publications

Automated Detection and Segmentation of Large Lesions in CT Colonography

Automated Detection and Segmentation of Large Lesions in CT Colonography

Colon Flattening Using Heat Diffusion Riemannian Metric

A Complete System for Polyps Flagging in Virtual Colonoscopy

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