Automated methods to delineate brain structures of interest are required to analyze large amounts of imaging data like
that being collected in several on going multi-center studies. We have previously reported on using artificial neural
networks (ANN) to define subcortical brain structures such as the thalamus (0.825), caudate (0.745), and putamen
(0.755). One of the inputs into the ANN is the apriori probability of a structure existing at a given location. In this
previous work, the apriori probability information was generated in Talairach space using a piecewise linear registration.
In this work we have increased the dimensionality of this registration using Thirion's demons registration algorithm. The
input vector consisted of apriori probability, spherical coordinates, and an iris of surrounding signal intensity values. The
output of the neural network determined if the voxel was defined as one of the N regions used for training. Training was
performed using a standard back propagation algorithm. The ANN was trained on a set of 15 images for 750,000,000
iterations. The resulting ANN weights were then applied to 6 test images not part of the training set. Relative overlap
calculated for each structure was 0.875 for the thalamus, 0.845 for the caudate, and 0.814 for the putamen. With the
modifications on the neural net algorithm and the use of multi-dimensional registration, we found substantial
improvement in the automated segmentation method. The resulting segmented structures are as reliable as manual raters
and the output of the neural network can be used without additional rater intervention.