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Titlebook: Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2010; 13th International C Tianzi Jiang,Nassir Navab,Max A. Viergever

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Incorporating Priors on Expert Performance Parameters for Segmentation Validation and Label Fusion: uld be carried out in a manner that accounts for the missing information. In other applications, locally inconsistent segmentations may drive the STAPLE algorithm into an undesirable local optimum, leading to misclassifications or misleading experts performance parameters..We present a new algorithm
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Construction of Neuroanatomical Shape Complex Atlas from 3D Brain MRIance transforms into probability density functions. Furthermore, square-root density functions can be seen as points on a unit hypersphere whose Riemannian structure is fully known. A shape complex atlas is constructed by first computing the Karcher mean . of the wave functions, followed by an inver
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Non-parametric Iterative Model Constraint Graph min-cut for Automatic Kidney Segmentationre generated by averaging three manual segmentations. Our method yields an average volumetric overlap error of 10.95%, and average symmetric surface distance of 0.79mm. These results indicate that our method is accurate and robust for kidney segmentation..Additional material can be found at ..
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Synthetic MRI Signal Standardization: Application to Multi-atlas Analysisnsfered to the synthetic scans to build a dataset-tailored gold standard. The approach was tested on a multi-atlas based hippocampus segmentation framework using a publicly available database, significantly improving the results obtained with other intensity correction methods.
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Cross-Visit Tumor Sub-segmentation and Registration with Outlier Rejection for Dynamic Contrast-Enhaoutlier time series. We obtained spatially-contiguous clusters that map to regions with distinct microvascular characteristics. This methodology has the potential to uncover localized effects in trials using DCE-MRI-based biomarkers.
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Conference proceedings 2010ious two conferences in New York and London. Three program chairs and a program committee of 31 scientists, all with a recognized standing in the ?eld of the conference, were responsible for the selection of the papers. The review process was set up such that each paper was considered by the three p
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Combining Morphological Information in a Manifold Learning Framework: Application to Neonatal MRI We present a framework in which multiple measures are used in manifold learning steps to generate coordinate embeddings which are then combined to give an improved single representation of the population. An application to neonatal brain MRI data shows that the use of shape and appearance measures
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