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 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

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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