<title>Sensitivity analysis of brain morphometry based on MRI-derived surface models</title>

Abstract

ABSTRACT Quantification of brain structure is important for evaluating changes in brain size with growth and aging and for character-izing neurodegeneration disorders. Previous quantification efforts using ex vivo techniques suffered considerable error due toshrinkage of the cerebrum after extraction from the skull, deformation of slices during sectioning, and numerous other factors.In vivo imaging studies of brain anatomy avoid these problems and allow repetitive studies following progression of brainstructure changes due to disease or natural processes. We have developed a methodology for obtaining triangular mesh modelsof the cortical surface from MRI brain datasets. The cortex is segmented from nonbrain tissue using a 2D region-growing tech-nique combined with occasional manual edits. Once segmented, thresholding and image morphological operations (erosionsand openings) are used to expose the regions between adjacent surfaces in deep cortical folds. A 2D region-following proce-dure is then used to find a set of contours outlining the cortical boundary on each slice. The contours on all slices are tiledtogether to form a closed triangular mesh model approximating the cortical surface. This model can be used for calculation ofcortical surface area and volume, as well as other parameters of interest. Except for the initial segmentation of the cortex fromthe skull, the technique is automatic and requires only modest computation time on modern workstations.Though the use of image data avoids many of the pitfalls of ex vivo and sectioning techniques, our MRI-based technique isstill vulnerable to errors that may impact the accuracy of estimated brain structure parameters. Potential inaccuracies includesegmentation errors due to incorrect thresholding, missed deep sulcal surfaces, falsely segmented holes due to image noise andsurface tiling artifacts. The focus of this paper is the characterization of these errors and how they affect measurements of cor-tical surface area and volume.Keywords: brain morphometry, surface models, segmentation