Measurement of brain structures with artificial neural networks: Two- and three-dimensional applications

identified with this method. PURPOSE: To evaluate the ability of an artifical-neural network (ANN) to identify brain structures. This ANN was applied : to;postprocess magnetic;resonance (MR) images to segment Various brain structures in both two- and three-dimensional applications. MATERIALS AND METHODS: An ANN was designed that learned from experience to define the corpus callosum, whole brain, caudate, and putamen. Manual segmentation was used asa training set for the ANN. The ANN was trained on two-thirds of the manually segmented images and was tested on the remaining one-third. The reliability of the ANN was compared against manual segmentations by two technicians. RESULTS: The ANN was able to identify the brain structures as readily and as well as did the two technicians. Reliability of the ANN compared with the technicians was 0.96 for the corpus callosum, 0.95 for the whole brain, 0.86 right and 0.93 (left) for the caudate,and 0.71 (right) and 0.88 (left) for the putamen. CONCLUSION:The ANN was able to identify the structures used inthisstudy as well as did the two technicians. The ANN could do this much more rapidly and without rater drift. Several;other; cortical and:-subcortical structures could also be readily identified with this method.