Registration and machine learning-based automated segmentation of subcortical and cerebellar brain structures

• Published in: NeuroImage (Orlando, Fla.) Vol. 39; no. 1; pp. 238 - 247
• Main Authors: Powell, Stephanie, Magnotta, Vincent A., Johnson, Hans, Jammalamadaka, Vamsi K., Pierson, Ronald, Andreasen, Nancy C.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.01.2008; Elsevier Limited
• Subjects: Algorithms; Artificial Intelligence; Artificial neural networks; Automation; Brain segmentation; Cerebellum - anatomy & histology; Cerebral Cortex - anatomy & histology; Classification; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Magnetic Resonance Imaging - methods; Methods; MRI; Neural networks; Pattern Recognition, Automated - methods; Probability; Registration-based segmentation; Reproducibility of Results; Sensitivity and Specificity; Standard deviation; Studies; Subtraction Technique; Support vector machine; Brain segmentation; Support vector machine; MRI; Registration-based segmentation; Artificial neural networks
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2007.05.063

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.