title: Registration and machine learning-based automated segmentation of subcortical and cerebellar brain structures.
abstract: The large amount of imaging data collected in several ongoing multi-center studies requires automated methods to delineate brain structures of interest. We have previously reported on using artificial neural networks (ANN) to define subcortical brain structures. Here we present several automated segmentation methods using multidimensional registration. A direct comparison between template, probability, artificial neural network (ANN) and support vector machine (SVM)-based automated segmentation methods is presented. Three metrics for each segmentation method are reported in the delineation of subcortical and cerebellar brain regions. Results show that the machine learning methods outperform the template and probability-based methods. Utilization of these automated segmentation methods may be as reliable as manual raters and require no rater intervention.
authors: Department of Radiology, The University of Iowa, Iowa City, Iowa 52242-1057, USA.
authors: Stephanie
authors: S
authors: Powell
authors: Vincent A
authors: VA
authors: Magnotta
authors: Hans
authors: H
authors: Johnson
authors: Vamsi K
authors: VK
authors: Jammalamadaka
authors: Ronald
authors: R
authors: Pierson
authors: Nancy C
authors: NC
authors: Andreasen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002531:Cerebellum / Q000033:anatomy & histology*; D002540:Cerebral Cortex / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique