Reduction of false positives on the rectal tube in computer-aided detection for CT colonography

2004 ◽  
Vol 31 (10) ◽  
pp. 2855-2862 ◽  
Author(s):  
Gheorghe Iordanescu ◽  
Ronald M. Summers
Keyword(s):  
Ct Colonography ◽  
False Positives ◽  
Rectal Tube ◽  
Computer Aided Detection ◽  
Computer Aided

Projection-based features for reducing false positives in computer-aided detection of colonic polyps in CT colonography

2010 ◽  
Author(s):  
Hongbin Zhu ◽  
Matthew Barish ◽  
Perry Pickhardt ◽  
Yi Fan ◽  
Erica Posniak ◽  
...  
Keyword(s):  
Ct Colonography ◽  
Colonic Polyps ◽  
False Positives ◽  
Computer Aided Detection ◽  
Computer Aided

Influence of Computer-Aided Detection False-Positives on Reader Performance and Diagnostic Confidence for CT Colonography

2009 ◽  
Vol 192 (6) ◽  
pp. 1682-1689 ◽  
Author(s):  
Stuart A. Taylor ◽  
John Brittenden ◽  
James Lenton ◽  
Hannah Lambie ◽  
Anthony Goldstone ◽  
...  
Keyword(s):  
Ct Colonography ◽  
False Positives ◽  
Computer Aided Detection ◽  
Diagnostic Confidence ◽  
Computer Aided

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.

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

CT Colonography with Computer-aided Detection as a Second Reader: Observer Performance Study

Radiology ◽  
2008 ◽  
Vol 246 (1) ◽  
pp. 148-156 ◽  
Author(s):  
Nicholas Petrick ◽  
Maruf Haider ◽  
Ronald M. Summers ◽  
Srinath C. Yeshwant ◽  
Linda Brown ◽  
...  
Keyword(s):  
Ct Colonography ◽  
Observer Performance ◽  
Computer Aided Detection ◽  
Performance Study ◽  
Computer Aided

scholarly journals Increasing computer-aided detection specificity by projection features for CT colonography

2010 ◽  
Vol 37 (4) ◽  
pp. 1468-1481 ◽  
Author(s):  
Hongbin Zhu ◽  
Zhengrong Liang ◽  
Perry J. Pickhardt ◽  
Matthew A. Barish ◽  
Jiangsheng You ◽  
...  
Keyword(s):  
Ct Colonography ◽  
Computer Aided Detection ◽  
Computer Aided ◽  
Detection Specificity

Computer-Aided Detection of Polyps in CT Colonography by Means of Feature Selection and Massive-Training Support Vector Regression

2012 ◽  
pp. 178-201
Author(s):  
Jian-Wu Xu ◽  
Kenji Suzuki
Keyword(s):  
Feature Selection ◽  
Support Vector Regression ◽  
Ct Colonography ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Computer Aided Detection ◽  
Training Time ◽  
Computer Aided ◽  
Wilks Lambda

One of the major challenges in current Computer-Aided Detection (CADe) of polyps in CT Colonography (CTC) is to improve the specificity without sacrificing the sensitivity. If a large number of False Positive (FP) detections of polyps are produced by the scheme, radiologists might lose their confidence in the use of CADe. In this chapter, the authors used a nonlinear regression model operating on image voxels and a nonlinear classification model with extracted image features based on Support Vector Machines (SVMs). They investigated the feasibility of a Support Vector Regression (SVR) in the massive-training framework, and the authors developed a Massive-Training SVR (MTSVR) in order to reduce the long training time associated with the Massive-Training Artificial Neural Network (MTANN) for reduction of FPs in CADe of polyps in CTC. In addition, the authors proposed a feature selection method directly coupled with an SVM classifier to maximize the CADe system performance. They compared the proposed feature selection method with the conventional stepwise feature selection based on Wilks’ lambda with a linear discriminant analysis classifier. The FP reduction system based on the proposed feature selection method was able to achieve a 96.0% by-polyp sensitivity with an FP rate of 4.1 per patient. The performance is better than that of the stepwise feature selection based on Wilks’ lambda (which yielded the same sensitivity with 18.0 FPs/patient). To test the performance of the proposed MTSVR, the authors compared it with the original MTANN in the distinction between actual polyps and various types of FPs in terms of the training time reduction and FP reduction performance. The authors’ CTC database consisted of 240 CTC datasets obtained from 120 patients in the supine and prone positions. With MTSVR, they reduced the training time by a factor of 190, while achieving a performance (by-polyp sensitivity of 94.7% with 2.5 FPs/patient) comparable to that of the original MTANN (which has the same sensitivity with 2.6 FPs/patient).

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