An artificial neural network for lesion detection on single-photon emission computed tomographic images.
OBJECTIVE
An artificial neural network (ANN) has been developed to detect nonactive circular lesions on single-slice, single-photon emission computed tomographic (SPECT) images reconstructed using filtered back projection (FBP).


METHODS
The neural network is a single-layer perception which learns to identify features on the SPECT image using supervised training with a modified delta rule. The network was trained on a set of SPECT images containing clinically realistic levels of noise. The trained network was applied to a set of 120 images, and the detection performance was evaluated at several decision thresholds using receiver operating characteristic (ROC) analysis.


RESULTS
The trained neural network performed better than human observers for the same detection task with the same images as reflected by a significantly larger ROC curve area.


CONCLUSIONS
ANN can be trained successfully to perform lesion detection on reconstructed SPECT images.
Floyd
C E
CE
Department of Radiology, Duke University Medical Center, Duke University, Durham, North Carolina 27710.
Tourassi
G D
GD
D004867:Equipment Design; D006801:Humans; D008961:Models, Structural; D016571:Neural Networks, Computer; D015588:Observer Variation; D012372:ROC Curve; D012985:Software Design; D015899:Tomography, Emission-Computed, Single-Photon / Q000295:instrumentation* / Q000379:methods / Q000706:statistics & numerical data