scholarly journals CT colonography: Advanced computer-aided detection scheme utilizing MTANNs for detection of “missed” polyps in a multicenter clinical trial

2009 ◽  
Vol 37 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Kenji Suzuki ◽  
Don C. Rockey ◽  
Abraham H. Dachman
Keyword(s):  
Clinical Trial ◽  
Ct Colonography ◽  
Computer Aided Detection ◽  
Multicenter Clinical Trial ◽  
Detection Scheme ◽  
Computer Aided ◽  
Advanced Computer

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).

Is the Computer-Aided Detection Scheme for Lung Nodule Also Useful in Detecting Lung Cancer?

2008 ◽  
Vol 32 (4) ◽  
pp. 570-575 ◽  
Author(s):  
Jin Mo Goo ◽  
Hyae Young Kim ◽  
Jeong Won Lee ◽  
Hyun Ju Lee ◽  
Chang Hyun Lee ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Lung Nodule ◽  
Computer Aided Detection ◽  
Detection Scheme ◽  
Computer Aided
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