Abstract-Volume-of-interest (VOI) extraction for radionuclide and anatomical measurements requires correct identification and delineation of the anatomical feature being studied. We have developed a toolset for specifying three-dimensional (
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.
Volume of interest extraction for radionuclide and anatomical measurements requires correct identification of the anatomical feature being studied. We have developed a toolset for specifying 3D volumes-of-interest (VOIs) on a multislice Positron Emission Tomography (PET) dataset. The software is particularly suited for specifying cerebral cortex VOIs that represent a particular gyrus or mid-brain structure. A registered 3D magnetic resonance image (MRI) dataset is used to provide high-resolution anatomical information, both as oblique 2D sections and as volume renderings of a segmented cortical surface. Because most clinicians can readily identify specific sulci from high-quality renderings of the cortical surface, a crucial step in quickly identifying sulci in 2D sectional data is providing a feedback mechanism between the renderings and the section data. Our toolkit provides this mechanism by calculating a full depth map and transformation matrix for volume renderings of the cortex. A region drawing environment is then possible where the position of a main drawing cursor on a 2D section can be simultaneously mirrored on the rendered views.The feedback is further enhanced by displaying the projection of the main cursor position on any number of auxiliary 2D sections oriented at orthogonal slicing angles with respect to the principal 2D section. Complete VOIs are specified by drawing a stack of 2D contours subsequently tiled together to form closed triangular mesh surface models,
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.