No abstract

Explicit segmentation is required for many forms of quantitative neuroanatomic analysis. However, manual methods are time-consuming and subject to errors in both accuracy and reproducibility (precision). A 3-D model-based segmentation method is presented in this paper for the completely automatic identification and delineation of gross anatomical structures of the human brain based on their appearance in magnetic resonance images (MRI).The approach depends on a general, iterative, hierarchical non-linear registration procedure and a 3-D digital model of human brain anatomy that contains both volumetric intensity-based data and a geometric atlas. Here, the traditional segmentation strategy is inverted: instead of matching geometric contours from an idealized atlas directly to the MRI data, segmentation is achieved by identifying the non-linear spatial transformation that best maps corresponding intensity-based features between a model image and a new MRI brain volume. When completed, atlas contours defined on the model image are mapped through the same transformation to segment and label individual structures in the new data set.Using manually segmented structure boundaries for comparison, measures of volumetric difference and volumetric overlap were less than 2% and better than 97% for realistic brain phantom data, and less than 10% and better than S5%, respectively, for human MRI data. This compares favorably to intra-observer variability estimates of 4.9% and 877'0, respectively. The procedure performs well, is objective and its implementation robust. The procedure requires no manual intervention, and is thus applicable to studies of large numbers of subjects. The general method for non-linear image matching is also useful for non-linear mapping of brain data sets into stereotaxic space if the target volume is already in stereotaxic space. a 1995 wiley-Liss, Inc.

We performed MRI volumetric measurements of the amygdala (AM), the hippocampal formation (HF), and the anterior temporal lobe in a group of 30 patients with intractable temporal lobe epilepsy (TLE) and in seven patients with extratemporal lobe foci. Measurements were analyzed with a semiautomated software program and the results compared with those of normal controls and correlated with the findings of all other investigations. In particular, we compared the results with the lateralization of epileptic abnormalities in the EEG. Volumetric studies of AM and HF showed lateralization of measurable atrophy consistent with that derived from extracranial and intracranial EEG examinations. The HF volumes were more sensitive and provided a lateralization in 87%. Combined measurements of AM and HF showed lateralization in 93%, always congruent with the results of EEG lateralization. This slight but important additional improvement in discrimination justifies using AM measurements in MRI volumetric studies of mesial temporal structures. Volumetric studies combined with other currently employed noninvasive techniques may diminish the need for invasive methods of investigation in patients with TLE.