1. Automatic pipeline for quantitative brain tissue segmentation and parcellation: Experience with a large longitudinal schizophrenia MRI study 1,2 G Gerig, 3,2 S Joshi, 1 H Gu, 1 D Perkins, 1 RG Steen, 1 R Hamer, 1 M Jomier, 1 JA Lieberman Dept. of 1 Psychiatry, 2 Computer Science, 3 Radiation Onc. University of North Carolina, Chapel Hill, NC 27614, USA gerig@cs.unc.edu / http://www.cs.unc.edu/~gerig March 2005: 1 Summary Introduction: Quantitative large-scale imaging studies require processing pipelines that are mostly automatic, reliable, generic in regard to differences in imaging protocols, and rigorously tested for reproducibility. Such software, if distributed to other sites, could help with cross-validation of results obtained at other various sites and thus meet concerns in regard to processing-specific results. We have developed software within the open- source Insight Toolkit (ITK, National Library of Medicine), that includes registration to standard coordinates, brain tissue segmentation, inhomogeneity correction, brain stripping, and brain lobe parcellation. Methods: An ITK implementation of automatic brain segmentation from multi-modal MRI has been combined with a brain parcellation based on template deformation. This results in an automatic pipeline for efficient processing of large image databases to provide reliable estimates of gray matter, white matter and cerebrospinal fluid. A parcellation template is obtained by an expert’s parcellation of an average MRI image. This template is deformed to each subject’s MRI by diffeomorphic large scale registration. We applied this new method to a large first-episode longitudinal schizophrenia study with 91 FE patients and 37 controls at baseline and 48 patients and 26 controls at 6 month follow-up. All MRI was performed on a 1.5T GE Signa, using T1-w gradient echo and dual-echo T2w/PDw spin- echo protocols. Results: Reliability of tissue segmentation was tested on data from a multi- site reliability study with the same subject imaged at 5 sites two times. Reproducibility of full brain tissue volumes were below 1%, demonstrating the excellent stability of segmentation. Reliability of the parcellation was tested with two cross-validation studies using two manually parcellated templates and qualitative assessment by three experts. March 2005: 2 METHODS Subjects Expectation Maximization Segmentation algorithm (EMS) Single or multiple MRI contrasts Multi-modal MRI and Atlas Registration Built in bias correction Initialization & classification governed by statistical atlas Built in brain stripping Efficient: 15 minutes Van Leemput et al.., Automated model- based tissue classification of MR images of the brain, IEEE TMI, 18(10), 1999Initializationclassification distribution estimation bias estimation MRI Brain Tissue Segmentation Multi-site, Multi-scanner Validation of Segmentation Dataset: Same subject scanned 2-times (24 hour window) at 5 different sites (4 GE, 1 Philips) within 60 days Automatic brain tissue segmentation using three- channel (T1, T2w, PDw) MRI Results show excellent reliability and stability of multi- site scanning and brain tissue segmentation M. Styner, C. Charles, J. Park, G. Gerig, Multisite validation of image analysis methods - Assessing intra and inter site variability, Proc. SPIE MedIm ‘02, 09/2002

2. RESULTS CONCLUSIONS Software Environment March 2005: 3 March 2005: 4 Results Clinical Study: ICOS 2005 #116401 Brain Lobe Parcellation Manual Parcellation Atlas based on 5 Subjects (Warped- Average) Warping Atlas (average MRI and labels) Parcellation Template Application to large study Validation of automatic parcellation Cross-validation of 2 labeled brains Cross-validation of one labeled subject versus warped atlas labeling Visual qualitative check of major sulci (3 raters) based on deviation score Parcellation by template warping: Precision depends on anatomical structure Imagine Image Processing Pipeline GUI: Modules are connected to produce a pipeline with several filters. This example shows how to use Imagine for tissue segmentation and quantification of a large set of brain MRI. UNC NeuroLib: Insight Toolkit (ITK) developments (National Library of Medicine Initiative). Tool freely available to research community (www.ia.unc.edu/dev, Matthieu Jomier)www.ia.unc.edu/dev Longitudinal Changes in Brain Volume in Patients with First-Episode Schizophrenia: An Exploratory Analysis of 91 Patients R. Steen; G. Gerig; H. Gu; D. Perkins; R. Hamer; J. Lieberman There were no significant differences between 91 patients and 37 controls at baseline. However, in both patients and controls, there was a significant (p < 0.02) decrease in frontal gray matter (GM) volume over 6 months, which may be age-related. There were also significant volumetric changes over 6 months that were unique to patients, including: a 5% increase in ventricular volume (p = 0.0009); a 1% increase in whole brain white matter (WM) volume (p = 0.02); a 2% increase in parietal WM volume (p = 0.006); and a 5% increase in occipital WM volume (p = 0.01). Automatic MRI analysis: Excellent reproducibility, no rater variability Suitable for large multi-site studies Multi-contrast MRI: Analysis extended to wm/gm/csf inter-relationships Lobe parcellation for regional assessment Standardized Software: Avoids site-specific results which are difficult to confirm. Allows cross-center validation Longitudinal Study: Implicit validation via controls (assuming no longitudinal change) “Calibration” of variability of whole system (MRI, biological variability, segmentation variability) Precision of full-brain tissue volumetry is in the range of 1% → Effect size needs to be larger. Lobe volumetry presents larger error. Longitudinal Assessment Validation of Segmentation Reliability via Longitudinal Study Controls (N=25, baseline and 6mt follow-up) Mean Time1Mean Time2Mean Abs Diff%Mean Abs DiffMean Diff%Mean Diff ICV1320676.01318887.67386.30.56%-1788.4-0.14% White453187.8452786.34603.71.02%-401.5-0.09% Gray657694.3651534.06835.21.04%-6160.3-0.94% Csf209793.8214567.35719.62.70%4773.42.25% Construction of MRI template via unbiased building of average template S. Joshi, B. Davis, M. Jomier, and G. Gerig, “Unbiased Diffeomorphic Atlas Construction for Computational Anatomy, NeuroImage; 23 (1). Expert subdivision into major lobar and subcortical structures Nonlinear deformation (fluid high-dimensional deformation) of average template to each subject Deformation of parcellation map  automatic parcellation Combination of individual parcellation with tissue segmentation (gm,wm,csf) ׃׃