Primary 2D Versus Primary 3D Polyp Detection at Screening CT Colonography

2007 ◽  
Vol 189 (6) ◽  
pp. 1451-1456 ◽  
Author(s):  
Perry J. Pickhardt ◽  
Andrew D. Lee ◽  
Andrew J. Taylor ◽  
Steven J. Michel ◽  
Thomas C. Winter ◽  
...  
Keyword(s):  
Ct Colonography ◽  
Polyp Detection

Eigenvalue-weighting and feature selection for computer-aided polyp detection in CT colonography

2010 ◽  
Author(s):  
Hongbin Zhu ◽  
Su Wang ◽  
Yi Fan ◽  
Hongbing Lu ◽  
Zhengrong Liang
Keyword(s):  
Feature Selection ◽  
Ct Colonography ◽  
Polyp Detection ◽  
Computer Aided ◽  
Selection For

T1229 CT Colonography: Polyp Detection and Endoscopic Follow up in an Urban UK Hospital Cohort

2010 ◽  
Vol 138 (5) ◽  
pp. S-516
Author(s):  
Lolita Chan ◽  
Javaid Iqbal ◽  
Andrew Mcpartlin ◽  
Tuheen Huda ◽  
Robert P. Willert
Keyword(s):  
Ct Colonography ◽  
Polyp Detection

Validating Pareto optimal operation parameters of polyp detection algorithms for CT colonography

2007 ◽  
Author(s):  
Jiang Li ◽  
Adam Huang ◽  
Nicholas Petrick ◽  
Jianhua Yao ◽  
Ronald M. Summers
Keyword(s):  
Ct Colonography ◽  
Optimal Operation ◽  
Pareto Optimal ◽  
Polyp Detection ◽  
Detection Algorithms ◽  
Operation Parameters

The use of 3D surface fitting for robust polyp detection and classification in CT colonography

2006 ◽  
Vol 30 (8) ◽  
pp. 427-436 ◽  
Author(s):  
Tarik A. Chowdhury ◽  
Paul F. Whelan ◽  
Ovidiu Ghita
Keyword(s):  
Ct Colonography ◽  
Surface Fitting ◽  
Polyp Detection ◽  
3D Surface

scholarly journals Improved diagnosis and navigation for CT colonography

2021 ◽  
Author(s):  
Jinjie Ming
Keyword(s):  
Ct Colonography ◽  
Automatic Segmentation ◽  
Segmentation Method ◽  
Polyp Detection ◽  
Data Set ◽  
Colon Wall ◽  
Texture Information ◽  
Ct Data ◽  
New Feature ◽  
Shape And Size

This project describes the development of an automatic segmentation method and a novel navigation system that detect polyps using advanced image processing and computer graphics tecniques. The colon wall segmentation method from the CT data set of abdomen is achieved by combining the contouring model - level set method and the minima detection using mathematical morphology theory. Polyp detection is attained by analyzing surface curvature and texture information along on the colon wall. Adding texture analysis provides a new feature for improving currently existing methods. As such, polyp candidates are examined not only by their shape and size but also by their texture appearance.

scholarly journals A Pipeline for Computer Aided Polyp Detection and Segmentation in Ct Colonography

2014 ◽  
Vol 9 (4) ◽  
pp. 68-75
Author(s):  
Janapriya A.S ◽  
◽  
Mythili C ◽  
Nishanthi P ◽  
Sunitha K
Keyword(s):  
Ct Colonography ◽  
Polyp Detection ◽  
Computer Aided

scholarly journals Improved diagnosis and navigation for CT colonography

2021 ◽  
Author(s):  
Jinjie Ming
Keyword(s):  
Ct Colonography ◽  
Automatic Segmentation ◽  
Segmentation Method ◽  
Polyp Detection ◽  
Data Set ◽  
Colon Wall ◽  
Texture Information ◽  
Ct Data ◽  
New Feature ◽  
Shape And Size

This project describes the development of an automatic segmentation method and a novel navigation system that detect polyps using advanced image processing and computer graphics tecniques. The colon wall segmentation method from the CT data set of abdomen is achieved by combining the contouring model - level set method and the minima detection using mathematical morphology theory. Polyp detection is attained by analyzing surface curvature and texture information along on the colon wall. Adding texture analysis provides a new feature for improving currently existing methods. As such, polyp candidates are examined not only by their shape and size but also by their texture appearance.

scholarly journals Re-initiation of CT colonography services during the COVID-19 pandemic: Preliminary evaluation of safety

2021 ◽  
Vol 94 (1121) ◽  
pp. 20201316
Author(s):  
David Peprah ◽  
Andrew Plumb ◽  
Alison Corr ◽  
Janice Muckian ◽  
Kathryn Smith ◽  
...  
Keyword(s):  
Ct Colonography ◽  
Recovery Phase ◽  
Service Evaluation ◽  
Preliminary Evaluation ◽  
Polyp Detection ◽  
Early Recovery ◽  
Detection Rates ◽  
Pulmonary Changes ◽  
Lung Base

Objective: The COVID-19 pandemic has led to cancellation and deferral of many cancer investigations, including CT colonography (CTC). In May 2020, BSGAR and SCoR issued guidelines outlining steps for conduct of CTC in the early recovery phase. We evaluated the implementation of these in four English hospital trusts. Methods: Ethical permission was not required for this multicentre service evaluation. We identified patients undergoing CTC over a 2-month period from May to July 2020 at four Trusts. We recorded demographics, scan indications, colonic findings, and incidental lung base changes compatible with COVID-19. A subset of patients were contacted via telephone to document new symptoms 2 weeks following their scan. Staff were contacted to determine if any acquired COVID-19 during the period. Results: 224 patients (118 male, 52.7%) were scanned during the period. In 55 patients (24.6%), CTC showed a ≥6 mm polyp. 33 of 224 (14.7%) scans showed incidental lung base changes felt unrelated to COVID-19, and only one patient had changes indeterminate for COVID-19; no classic COVID-19 pulmonary changes were found. Of 169 patients with telephone follow-up, none reported any new symptoms of COVID-19 (cough, fever, anosmia, ageusia) within 14 days of CTC. None of the 86 staff contacted developed COVID-19. Conclusion: We found no cases of patients or staff acquiring COVID-19 infection following CTC; and no evidence of significant asymptomatic COVID-19 patients attending for CTC appointments based on lung base changes. Advances in knowledge: Our findings suggest that current practice is unlikely to contribute significantly to spread of SARS-nCOV2. Cancer and significant polyp detection rates were high, underlining the importance of maintaining service provision.

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