Computer-assisted detection of colonic polyps with CT colonography using neural networks and binary classification trees.
Detection of colonic polyps in CT colonography is problematic due to complexities of polyp shape and the surface of the normal colon. Published results indicate the feasibility of computer-aided detection of polyps but better classifiers are needed to improve specificity. In this paper we compare the classification results of two approaches: neural networks and recursive binary trees. As our starting point we collect surface geometry information from three-dimensional reconstruction of the colon, followed by a filter based on selected variables such as region density, Gaussian and average curvature and sphericity. The filter returns sites that are candidate polyps, based on earlier work using detection thresholds, to which the neural nets or the binary trees are applied. A data set of 39 polyps from 3 to 25 mm in size was used in our investigation. For both neural net and binary trees we use tenfold cross-validation to better estimate the true error rates. The backpropagation neural net with one hidden layer trained with Levenberg-Marquardt algorithm achieved the best results: sensitivity 90% and specificity 95% with 16 false positives per study.
Jerebko
Anna K
AK
Department of Radiology, National Institutes of Health, 10 Center Drive, Bethesda, Maryland 20892-1182, USA.
Summers
Ronald M
RM
Malley
James D
JD
Franaszek
Marek
M
Johnson
C Daniel
CD
D000465:Algorithms; D016000:Cluster Analysis; D003111:Colonic Polyps / Q000000981:diagnostic imaging*; D023881:Colonography, Computed Tomographic / Q000379:methods*; D005189:False Positive Reactions; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012016:Reference Values; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity