AN ARTIFICIAL NEURAL NETWORK FOR LESION DETECTION ON SINGLE-PHOTON EMISSION COMPUTED TOMOGRAPHIC-IMAGES

RATIONALE AND OBJECTIVES. 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 perceptron 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.