Titlebook: Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2012; 15th International C Nicholas Ayache,Hervé Delingette,Kensaku Mo

CLIFF

A Hierarchical Scheme for Geodesic Anatomical Labeling of Airway Treese branch label configurations for a given tree are evaluated based on distances to a training set of labeled trees. In tree-space, the tree topology and geometry change continuously, giving a natural way to automatically handle anatomical differences and noise. The algorithm is made efficient using

壓碎

Geodesic Shape-Based Averagingtrained to one single slice and by guaranteeing spatial contiguity. The proposed method not only reduces the fragmentation of the propagated areas but also significantly increases the seed positioning accuracy and subsequent tractography results when compared to state-of-the-art label fusion techniques.

枯萎將要

Multi-scale Characterization of White Matter Tract Geometryes fibre dispersion, or fanning, along each direction on the circle. Our formulation is then easily incorporated into a continuous scale-space framework. We illustrate our method on different fibre tracts and apply it to a population study on hemispheric lateralization in healthy controls. We conclude with directions for future work.

阻礙

暴行

阻礙

Nerve-Block

Estimation and Reduction of Target Registration Errorwork, we present a new method to estimate the quality of a fiducial-based registration and show that our measure is correlated to the target registration error and that it can be used to reduce registration error caused by fiducial localization error. This has direct implication on the attainable accuracy of fiducial-based registration methods.

碳水化合物

廣口瓶

Conference proceedings 2012d Computer-Assisted Intervention, MICCAI 2012, held in Nice, France, in October 2012. Based on rigorous peer reviews, the program committee carefully selected 252 revised papers from 781 submissions for presentation in three volumes. The third volume includes 79 papers organized in topical sections

放大

Optimization of Acquisition Geometry for Intra-operative Tomographic Imagingsed on the singular value decomposition of the system matrix, and presents an approach to find optimal detector positions via a randomized optimization scheme. The feasibility of this approach is demonstrated using simulations of an intra–operative functional imaging setup and actual robot–controlled phantom experiments.