Three-dimensional lesion detection in SPECT using artificial neural networks

An artificial neural network was developed to perform lesion detection in Single Photon Emission Tomography (SPEC'l) using information from three consecutive slices. The network had a three-layer, feed-forward architecture. For the present study, the detection task was restricted to deciding the presence or absence of a lesion at a given location in the middle slice considering also the two adjacent slices. An 1 lxi 1 pixel neighborhood was extracted around the potential location of the lesion in every slice. The total 363 pixel values represented the input information given to the network. Then, the network was trained using the backpropagation algorithm to output 1 if a lesion was present in the middle slice and 0 if not. The diagnostic performance of the three-dimensional (3D) detection network was evaluated for various noise levels and lesion sizes. In addition, the 3D detection network was compared to a two-dimensional (2D) network trained to perform the same detection task based only on the middle slice. In all cases, the 3D network significantly outperformed the 2D network. This study shows the potential of feedforward, backpropagation networks to view multiple images simultaneously when performing a lesion detection task.