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The Schaltenbrand-Wahren (SW) brain atlas has many limitations: the major two are three-dimensional (3D) inconsistency and spatial sparseness. In this work, we quantify and visualize the 3D inconsistency of the subthalamic nucleus (STN). The STN 3D models, 3D-A, 3D-C and 3D-S, are reconstructed from the SW axial, coronal, and sagittal microseries,(More)
Segmentation of the left ventricle is important in the assessment of cardiac functional parameters. Manual segmentation of cardiac cine MR images for acquiring these parameters is time-consuming. Accuracy and automation are the two important criteria in improving cardiac image segmentation methods. In this paper, we present a comprehensive approach to(More)
The major shortcomings of the Schaltenbrand-Wahren (SW) brain atlas include 3-dimensional (3D) inconsistency and spatial sparseness. This work quantifies and visualizes 3D inconsistency of the globus pallidus internus (GPi), a stereotactic target for the treatment of Parkinson's disease, dystonia and Huntington disease. The GPi 3D models 3D-A, 3D-C and 3D-S(More)
Stereotactic human brain atlases, either in print or electronic form, are useful not only in functional neurosurgery, but also in neuroradiology, human brain mapping, and neuroscience education. The existing atlases represent structures on 2D plates taken at variable, often large intervals, which limit their applications. To overcome this problem, we(More)
We have used digitonin-permeabilized cells to examine in vitro nuclear export of glucocorticoid receptors (GRs). In situ biochemical extractions in this system revealed a distinct subnuclear compartment, which collects GRs that have been released from chromatin and serves as a nuclear export staging area. Unliganded nuclear GRs within this compartment are(More)
RATIONALE AND OBJECTIVES Accurate segmentation of the brain ventricular system on computed tomographic (CT) imaging is useful in neurodiagnosis and neurosurgery. Manual segmentation is time consuming, usually not reproducible, and subjective. Because of image noise, low contrast between soft tissues, large interslice distance, large shape, and size(More)
This paper presents an approach to automatically build a semantic perceptron net (SPN) for topic spotting. It uses context at the lower layer to select the exact meaning of key words, and employs a combination of context, co-occurrence statistics and thesaurus to group the distributed but semantically related words within a topic to form basic semantic(More)
Our aim is to develop an automatic method which can detect diverse focal liver lesions (FLLs) in 3D CT volumes.    A hybrid generative-discriminative framework is proposed. It first uses a generative model to describe non-lesion components and then identifies all candidate FLLs within a 3D liver volume by eliminating non-lesion components. It subsequently(More)
This work addresses the spatial correlation between the anatomical and functional human subthalamic nucleus (STN). The anatomical STN (A-STN), derived from the Schaltenbrand-Wahren brain atlas, is histology based. The functional STN (F-STN) is probabilistic, constructed from neuroelectrophysiological and neuroimaging data of 184 Parkinson's disease(More)