High-performance computer aided detection system for polyp detection in CT colonography with fluid and fecal tagging

2009 ◽  
Author(s):  
Jiamin Liu ◽  
Shijun Wang ◽  
Suraj Kabadi ◽  
Ronald M. Summers
Keyword(s):  
Ct Colonography ◽  
High Performance ◽  
Detection System ◽  
Computer Aided Detection ◽  
Polyp Detection ◽  
Fecal Tagging ◽  
Computer Aided ◽  
High Performance Computer

Pseudo-enhancement correction for computer-aided detection in fecal-tagging CT colonography

2007 ◽  
Author(s):  
Janne Näppi ◽  
Hiroyuki Yoshida ◽  
Michael Zalis ◽  
Wenli Cai ◽  
Philippe Lefere
Keyword(s):  
Ct Colonography ◽  
Computer Aided Detection ◽  
Fecal Tagging ◽  
Computer Aided

Digital bowel cleansing for computer-aided detection of polyps in fecal-tagging CT colonography

2006 ◽  
Author(s):  
Wenli Cai ◽  
Janne Näppi ◽  
Micheal E. Zalis ◽  
Gordon J. Harris ◽  
Hiroyuki Yoshida
Keyword(s):  
Ct Colonography ◽  
Computer Aided Detection ◽  
Fecal Tagging ◽  
Bowel Cleansing ◽  
Computer Aided

Computer-aided Detection in Computed Tomography Colonography with Full Fecal Tagging: Comparison of Standalone Performance of 3 Automated Polyp Detection Systems

2010 ◽  
Vol 61 (2) ◽  
pp. 102-108 ◽  
Author(s):  
Patrick A. Hein ◽  
Lasse D. Krug ◽  
Valentina C. Romano ◽  
Sonja Kandel ◽  
Bernd Hamm ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Data Sets ◽  
Computer Aided Detection ◽  
Polyp Detection ◽  
Cad Systems ◽  
Fecal Tagging ◽  
Computed Tomography Colonography ◽  
Computer Aided ◽  
System 2 ◽  
System 1

Purpose We sought to compare the performance of 3 computer-aided detection (CAD) polyp algorithms in computed tomography colonography (CTC) with fecal tagging. Methods CTC data sets of 33 patients were retrospectively analysed by 3 different CAD systems: system 1, MedicSight; system 2, Colon CAD; and system 3, Polyp Enhanced View. The polyp database comprised 53 lesions, including 6 cases of colorectal cancer, and was established by consensus reading and comparison with colonoscopy. Lesions ranged from 6-40 mm, with 25 lesions larger than 10 mm in size. Detection and false-positive (FP) rates were calculated. Results CAD systems 1 and 2 could be set to have varying sensitivities with higher FP rates for higher sensitivity levels. Sensitivities for system 1 ranged from 73%–94% for all lesions (78%–100% for lesions ≥10 mm) and, for system 2, from 64%–94% (78%–100% for lesions ≥10 mm). System 3 reached an overall sensitivity of 76% (100% for lesions ≥10 mm). The mean FP rate per patient ranged from 8–32 for system 1, from 1–8 for system 2, and was 5 for system 3. At the highest sensitivity level for all polyps (94%), system 2 showed a statistically significant lower FP rate compared with system 1 ( P = .001). When analysing lesions ≥10 mm, system 3 had significantly fewer FPs than systems 1 and 2 ( P < .012). Conclusions Standalone CTC-CAD analysis in the selected patient collective showed the 3 systems tested to have a variable but overall promising performance with respect to sensitivity and the FP rate.

Computer-Aided Detection of Colorectal Polyps in CT Colonography With and Without Fecal Tagging

2012 ◽  
Vol 47 (2) ◽  
pp. 99-108 ◽  
Author(s):  
Thomas Mang ◽  
Luca Bogoni ◽  
Marcos Salganicoff ◽  
Matthias Wolf ◽  
Vikas Raykar ◽  
...  
Keyword(s):  
Ct Colonography ◽  
Colorectal Polyps ◽  
Computer Aided Detection ◽  
Fecal Tagging ◽  
Computer Aided

An artificial intelligence-based quality improvement system significantly improved the efficacy of computer-aided detection system in colonoscopy: A four group parallel study

Endoscopy ◽  
2021 ◽  
Author(s):  
Liwen Yao ◽  
Lihui Zhang ◽  
Jun Liu ◽  
Wei Zhou ◽  
Chunping He ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Detection System ◽  
Synergetic Effect ◽  
Colorectal Polyp ◽  
Controlled Study ◽  
Computer Aided Detection ◽  
Polyp Detection ◽  
Adenoma Detection ◽  
Significant Difference ◽  
Computer Aided

Background and study aims: Tandem colonoscopy studies have found that about one in five adenomas are missed at colonoscopy. It is still debatable whether the combination of a computer-aided detection (CADe) system for colorectal polyp detection with a computer-aided quality improvement (CAQ) system for real-time withdrawal speed monitoring may result in additional benefits in the task of adenoma detection or if the synergetic effect may be harmed due to excessive visual burden resulting from the information overload. This study aims to evaluate the interaction effect on improving the adenoma detection rate (ADR). Patients and methods: This is a single-center, randomized, four-group parallel controlled study, performed in Renmin Hospital of Wuhan University. Between July 1, 2020 and Oct 15, 2020, 1076 participants were randomly allocated into four treatment groups [control: 271, CADe: 268, CAQ: 269 and CADe plus CAQ (COMBO): 268]. The primary outcome was the ADR. Results: The average ADR in the control, CADe, CAQ and COMBO groups was 14.76% (95% C.I. 10.54-18.98), 21.27% (95% C.I. 16.37-26.17), 24.54% (95% C.I. 19.39-29.68) and 30.6% (95% C.I. 25.08-36.11), respectively. The ADR was higher in the COMBO group compared with the CADe group but not compared with the CAQ group (21.27% VS 30.6%, P=0.024, OR 1.284, 95%C.I. 1.033-1.596; 24.54%vs. 30.6%, P = 0.213, OR = 1.309, 95% C.I. 0.857-2, respectively). Conclusions: CAQ significantly improved the efficacy of CADe in a four-group parallel controlled study. No significant difference in the ADR or PDR was found between the CAQ and COMBO groups.

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

2012 ◽  
pp. 220-237
Author(s):  
Xujiong Ye ◽  
Greg Slabaugh
Keyword(s):  
Ct Colonography ◽  
Colonic Polyps ◽  
Volume Effect ◽  
False Positives ◽  
Detection Sensitivity ◽  
Computer Aided Detection ◽  
Polyp Detection ◽  
Model Driven ◽  
Computer Aided ◽  
Overlap Ratio

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.

Performance of a Previously Validated CT Colonography Computer-Aided Detection System in a New Patient Population

2008 ◽  
Vol 191 (1) ◽  
pp. 168-174 ◽  
Author(s):  
Ronald M. Summers ◽  
Laurie R. Handwerker ◽  
Perry J. Pickhardt ◽  
Robert L. Van Uitert ◽  
Keshav K. Deshpande ◽  
...  
Keyword(s):  
Patient Population ◽  
Ct Colonography ◽  
Detection System ◽  
Computer Aided Detection ◽  
Computer Aided

Computer-Aided Detection of Colorectal Polyps at CT Colonography

Endoscopy ◽  
2004 ◽  
Vol 36 (05) ◽  
Author(s):  
RJT Sadleir ◽  
PF Whelan ◽  
N Sezille ◽  
TA Chowdhury ◽  
A Moss ◽  
...  
Keyword(s):  
Ct Colonography ◽  
Colorectal Polyps ◽  
Computer Aided Detection ◽  
Computer Aided
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