An Improved Electronic Colon Cleansing Method for Detection of Colonic Polyps by Virtual Colonoscopy

Wang, Zigang; Liang, Zach; Li, Xiang; Li, Lihong; Li, Bin; Eremina, D.; Lu, Hongbinbg

doi:10.1109/tbme.2006.876630

Cited by 24 publications

(45 citation statements)

References 36 publications

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“…Two performance criteria are used: Sensitivity (S) on the detected and removed tagging voxels versus all labeled tagging voxels; and Accuracy (A) on the number of removed tagging voxels versus all removed voxels. For comparison, we implement the Gaussian Mixture Model (GMM) based eCleansing method [1]. Our method has S = 99.2% and A = 99.3% while S = 93.6% and A = 98.9% are for our version of [1].…”

Section: Resultsmentioning

confidence: 99%

“…For comparison, we implement the Gaussian Mixture Model (GMM) based eCleansing method [1]. Our method has S = 99.2% and A = 99.3% while S = 93.6% and A = 98.9% are for our version of [1]. The main difference is on sensitivities.…”

Section: Resultsmentioning

confidence: 99%

“…Due to partial-volume effect in the air-tagging transition regions and air-taggingtissue T-junctions [1,2] in AT, there are certain amounts of air-tagging mixed, or pseudo voxels having very similar (single voxel) intensity value as soft-tissue. Therefore we formulate a high-order intensity pattern to differentiate pseudo and true soft-tissue voxels through context modeling.…”

Section: Multivariate Image Model and Mrf In Atmentioning

confidence: 99%

“…However, similar to traditional examination protocols, the colon is required to be fully cleansed manually or electronically. Electronic cleansing (eCleansing) of fecal residues by computed algorithms [1,2] is very critical to avoid intensive manual bowel preparation, prior to the examination. The quality of electronic cleansing method directly impacts radiologist's image reading performance in 3D, as well as computer-aided diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…This protocol however causes much higher imaging noises, inhomogeneous intensity patterns and lower contrast between tagging materials and surrounding softtissue structures. Significant challenges are imposed for radiologists and previous eCleansing methods [1,2], due to lack of precise statistical data-driven appearance model. To address this issue, our proposed algorithm is described as follows.…”

Section: Introductionmentioning

confidence: 99%

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A New Algorithm of Electronic Cleansing for Weak Faecal-Tagging CT Colonography

Lü

Jian

et al. 2013

Lecture Notes in Computer Science

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Abstract. CT Colonography (CTC) has emerged as a mainstream clinical practice of colonic cancer screening and diagnosis. One of the most critical problems is to increase compliance with CTC examinations via minimal bowel preparation (i.e., weak faecal-tagging), which nevertheless causes much lower signal-noise-ratio than conventional preparation.In this paper, we present a new algorithm pipeline of electronically cleansing tagging materials in CTC under reduced oral contrast dose. Our method has the following steps: 1, robust structure parsing to generate a list of volume regions of interest (ROIs) of tagging material (avoiding bone erosion); 2, effectively locating local tagging-air (AT) transitional surface regions; 3, a novel discriminative-generative algorithm to learn the higher-order image appearance model in AT using 3D Markov Random Fields (MRF); 4, accurate probability density function based voxel labeling corresponding to semantic classes. Validated on 26 weak faecaltagging CTC cases from 3 medical sites, our method yields better visualization clarity and readability compared with the previous approach [1]. The whole system computes efficiently (e.g., < 40 seconds for CT images of 512 × 512 × 1000+).

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Multivariate Image Model and Mrf In Atmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New Algorithm of Electronic Cleansing for Weak Faecal-Tagging CT Colonography

Lü

Jian

et al. 2013

Lecture Notes in Computer Science

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A Markov Random Field orientation prior for electronic cleansing in CT Colonography

Krishnan

Desai

2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Tagging of the bowel content with an oral contrast facilitates CT Colonography with limited bowel preparation. Electronic colon cleansing (ECC) reconstructs the colon lumen, devoid of feces from a CT scan acquired with fecal fluid tagging. A popular method to estimate the stool composition in an image (with the purpose of removing it) is the well-established Expectation Maximization (EM) method. The tagged fluid residue appears as a contrast enhanced region with a largely horizontal interface with air above it. One of the issues is the partial volume (PV) effect that creates voxels with attenuations similar to that of the colon wall at the boundary of air and tagged fluid. We present here, a novel orientation prior formulated as a Markov Random Field that is included as part of the EM tissue segmentation framework to mitigate this PV effect at the air and tagged fluid layer. We show quantitative results on a simple synthetic dataset and qualitative results on patient data that highlight improvements due to the inclusion of the orientation prior.

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CAD of colon cancer on CT colonography cases without cathartic bowel preparation

Linguraru

Zhao

Uitert³

et al. 2008

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Computer-aided diagnosis (CAD) systems must show sufficient versatility to produce robust analysis on a large variety of data. In the case of colonography, CAD has not been designed to cope with the presence of stool, although labeling the stool with high contrast agents replaces the use of laxatives and reduces the patient discomfort. This procedure introduces additional challenges for the diagnosis, such as poorly tagged stool, stool sticking to colonic walls, and heterogeneous stool (tagged stool mixed with air or untagged stool). Our study proposes a robust algorithm for heterogeneous stool removal to be employed as a preprocessing module for CAD systems in colonic cancer detection. Colonoscopy data are automatically cleansed of residual stool to enhance the polyp appearance for improved diagnosis. The algorithm uses expectation-maximization, quadratic regression, level sets and minimum variance. Results show stool removal accuracy on polyps which are partially or fully covered by stool. The results are robust on stool lining and large pools of heterogeneous and weakly-tagged stool. The automatic detection of colon polyps using our CAD system on cathartic-free data improves considerably with the addition of the automatic stool removal module from 74% to 86% true positive (TP) rate at 6.4 false positives (FP)/case.

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An Improved Electronic Colon Cleansing Method for Detection of Colonic Polyps by Virtual Colonoscopy

Cited by 24 publications

References 36 publications

A New Algorithm of Electronic Cleansing for Weak Faecal-Tagging CT Colonography

A New Algorithm of Electronic Cleansing for Weak Faecal-Tagging CT Colonography

A Markov Random Field orientation prior for electronic cleansing in CT Colonography

CAD of colon cancer on CT colonography cases without cathartic bowel preparation

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