th schizophrenia compared with controls (13.1%) and patients with bipolar mania (16.8%). Conclusions: Compared with controls and patients with bipolar manic psychosis, patients with first-episode schizophrenia showed left planum temporale gray matter volume reduction and bilateral Heschl gyrus gray matter volume reduction. These findings are similar to those reported in patients with chronic schizophrenia and suggest that such abnormalities are present at first episode and are specific to schizophrenia. Arch Gen Psychiatry. 2000;57:692-699 S TRUCTURAL brain abnormalities in schizophrenia have been extensively investigated using magnetic resonance imaging (MRI). 1-5 Several studies have reported abnormalities in portions of the superior temporal gyrus (STG) in patients diagnosed as having schizophrenia, 1-3 including planum temporale (PT) and Heschl gyrus (HG) (primary auditory cortex).<F6.

We present iCluster, a fast and efficient algorithm that clusters a set of images while co-registering them using a parameterized, nonlinear transformation model. The output of the algorithm is a small number of template images that represent different modes in a population. This is in contrast with traditional, hypothesis-driven computational anatomy approaches that assume a single template to construct an atlas. We derive the algorithm based on a generative model of an image population as a mixture of deformable template images. We validate and explore our method in four experiments. In the first experiment, we use synthetic data to explore the behavior of the algorithm and inform a design choice on parameter settings. In the second experiment, we demonstrate the utility of having multiple atlases for the application of localizing temporal lobe brain structures in a pool of subjects that contains healthy controls and schizophrenia patients. Next, we employ

Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. However, the availability of comprehensive, reliable and suitable manual segmentations for atlas construction is limited. We therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas. Instead, a latent atlas, initialized by a single manual segmentation, is inferred from the evolving segmentations of the ensemble. The proposed method is based on probabilistic principles but is solved using partial differential equations (PDEs) and energy minimization criteria. We evaluate the method by segmenting 50 brain MR volumes. Segmentation accuracy for cortical and subcortical structures approaches the quality of state-of-the-art atlas-based segmentation results, suggesting that the latent atlas method is a reasonable alternative when existing atlases are not compatible with the data to be processed.

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Abstract. We analyze the impact of atlas construction within the context of an atlas-guided segmenter applied to a morphometry study in neuroanatomy. Automatic segmenters often rely on anatomical information encoded via probabilistic atlases. These atlases are frequently constructed by registering collections of training data. In this paper, we study the impact of registration methods as well as the training data on automatic segmentation results. With respect to registration, we focus our comparison on pairwise vs. group-wise methods and fixed vs. online coordinate systems. For the training data, we consider collections of population specific and general population data. To study the impact of these factors, we revisit a previously published statistical group comparison that was based on manual segmentations. For each atlas type, we record the group differences based on automatic segmentations and compare these findings to the original ones. Furthermore, we measure the Dice overlap between manual and automatic segmentations. Our results indicate some advantages for coordinate systems that are developed in an online fashion. 1