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Measurement of Brain Structures with Artificial Neural Networks: Two- and Three-dimensional Applications¹

¹ Department of Radiology, Mental Health Clinical Research Center, the University of Iowa Hospitals and Clinics, 2911 JPP, 200 Hawkins Dr, Iowa City, IA 52242. Received July 10, 1998; revision requested August 13; revision received September 21; accepted November 13. Supported in part by National Institutes of Mental Health grants MH31593, MH40856, and MHCRC43271 and Research Scientist award MH00625, and an Established Investigator Award from the National Alliance for Research on Schizophrenia and Depression. Address reprint requests to V.A.M.

PURPOSE: To evaluate the ability of an artificial neural network (ANN) to identify brain structures. This ANN was applied to postprocessed 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 as a 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 in this study 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.

Index terms: Brain, anatomy, 10.92 • Brain, MR, 10.121411, 10.121412 • Computers, neural network, 10.92 • Images, analysis, 10.92 • Images, processing, 10.121411, 10.121412, 10.92

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