An artificial neural network for lesion detection on single-photon emission computed tomographic images.

Journal Article (Journal Article)

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 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.

Full Text

Duke Authors

Cited Authors

  • Floyd, CE; Tourassi, GD

Published Date

  • September 1992

Published In

Volume / Issue

  • 27 / 9

Start / End Page

  • 667 - 672

PubMed ID

  • 1399448

International Standard Serial Number (ISSN)

  • 0020-9996

Digital Object Identifier (DOI)

  • 10.1097/00004424-199209000-00001


  • eng

Conference Location

  • United States