Artificial neural networks for single photon emission computed tomography. A study of cold lesion detection and localization.
OBJECTIVE
An artificial neural network was developed for cold lesion detection and localization in single photon emission computed tomography (SPECT) images.


METHODS
The network was trained for several noise levels and lesion sizes to identify lesions located in the center of small image neighborhoods. When scrolled across an image the trained network was able to identify cold abnormalities. The diagnostic performance of the technique was evaluated at two noise levels (50,000 and 100,000 counts/slice) and for two lesion sizes (radius: 1.0 cm and 1.5 cm) using the free-response operating characteristic (FROC) analysis. Furthermore, the same network was tested on a situation it was not trained on (80,000 counts/slice and a different reconstruction filter).


RESULTS
The neural network showed high sensitivity and small false-positive rates per image for all test situations. These results suggest that neural networks are promising tools for computer-aided clinical diagnosis in SPECT:
Department of Biomedical Engineering, Duke University Medical Center, Durham, NC 27710.
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GD
Tourassi
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CE
Floyd
D004867:Equipment Design; D005069:Evaluation Studies as Topic; D005189:False Positive Reactions; D006801:Humans; D009010:Monte Carlo Method; D016571:Neural Networks, Computer; D012372:ROC Curve; D012680:Sensitivity and Specificity; D015899:Tomography, Emission-Computed, Single-Photon / Q000295:instrumentation*