Sensitivity analysis of brain morphometry based on MRI-derived surface models

Gregory J. Klein; Xia Teng; P. Thomas Schoenemann; Thomas F. Budinger

doi:10.1117/12.312575

3 July 1998 Sensitivity analysis of brain morphometry based on MRI-derived surface models

Gregory J. Klein, Xia Teng, P. Thomas Schoenemann, Thomas F. Budinger

Proceedings Volume 3337, Medical Imaging 1998: Physiology and Function from Multidimensional Images; (1998) https://doi.org/10.1117/12.312575
Event: Medical Imaging '98, 1998, San Diego, CA, United States

Abstract

Quantification of brain structure is important for evaluating changes in brain size with growth and aging and for characterizing neurodegeneration disorders. Previous quantification efforts using ex vivo techniques suffered considerable error due to shrinkage 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 brain structure changes due to disease or natural processes. We have developed a methodology for obtaining triangular mesh models of the cortical surface from MRI brain datasets. The cortex is segmented from nonbrain tissue using a 2D region-growing technique combined with occasional manual edits. Once segmented, thresholding and image morphological operations (erosions and openings) are used to expose the regions between adjacent surfaces in deep cortical folds. A 2D region- following procedure is then used to find a set of contours outlining the cortical boundary on each slice. The contours on all slices are tiled together to form a closed triangular mesh model approximating the cortical surface. This model can be used for calculation of cortical surface area and volume, as well as other parameters of interest. Except for the initial segmentation of the cortex from the 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 is still vulnerable to errors that may impact the accuracy of estimated brain structure parameters. Potential inaccuracies include segmentation errors due to incorrect thresholding, missed deep sulcal surfaces, falsely segmented holes due to image noise and surface tiling artifacts. The focus of this paper is the characterization of these errors and how they affect measurements of cortical surface area and volume.

Citation Download Citation

Gregory J. Klein, Xia Teng, P. Thomas Schoenemann, and Thomas F. Budinger "Sensitivity analysis of brain morphometry based on MRI-derived surface models", Proc. SPIE 3337, Medical Imaging 1998: Physiology and Function from Multidimensional Images, (3 July 1998); https://doi.org/10.1117/12.312575

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available