A variety of methodologies have been developed for the parcellation of human cortical surface into sulcal or gyral
regions due to its importance in structural and functional mapping of the human brain. However, characterizing the
performance of surface parcellation methods and the estimation of ground truth of segmentation are still open problems.
In this paper, we present an algorithm for simultaneous truth and performance estimation of various approaches for
human cortical surface parcellation. The probabilistic true segmentation is estimated as a weighted combination of the
segmentations resulted from multiple methods. Afterward, an Expectation-Maximization (EM) algorithm is used to
optimize the weighting depending on the estimated performance level of each method. Furthermore, a spatial
homogeneity constraint modeled by the Hidden Markov Random Field (HMRF) theory is incorporated to refine the
estimated true segmentation into a spatially homogenous decision. The proposed method has been evaluated using both
synthetic and real data. The experimental results demonstrate the validity of the method proposed in this paper. Also, it
has been used to generate reference sulci regions to perform a comparison study of three methods for cortical surface
parcellation.
In this paper, we present an approach of predictive modeling of neuroanatomic structures for the detection of brain
atrophy based on cross-sectional MRI image. The underlying premise of applying predictive modeling for atrophy
detection is that brain atrophy is defined as significant deviation of part of the anatomy from what the remaining normal
anatomy predicts for that part. The steps of predictive modeling are as follows. The central cortical surface under
consideration is reconstructed from brain tissue map and Regions of Interests (ROI) on it are predicted from other
reliable anatomies. The vertex pair-wise distance between the predicted vertex and the true one within the abnormal
region is expected to be larger than that of the vertex in normal brain region. Change of white matter/gray matter ratio
within a spherical region is used to identify the direction of vertex displacement. In this way, the severity of brain
atrophy can be defined quantitatively by the displacements of those vertices. The proposed predictive modeling method
has been evaluated by using both simulated atrophies and MRI images of Alzheimer's disease.
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