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Medical Image Analysis SeminarFriday April 2nd 9:00 - 12:00, DTU, IMM, room 053 building 321. Virtual Liver Surgery Planning System using Augmented Reality ------------------------------------------------------------- Milan Sonka, Iowa University A system for virtual planning of liver tumor resections will be discussed that consists of the environment for diaphragm, liver, liver tumor, and liver vasculature segmentation. The second part of the environment allows augmented reality planning of liver tumor resections. The system is under development and many individual modules will be discussed and their functionality presented. Detection and Quantification of Motion and Growth using Non-rigid Registration ------------------------------------------------------------- Daniel Rueckert, Imperial College Three-dimensional (3D) and four-dimensional (4D) imaging of dynamic structures like the heart is a rapidly developing area of research in medical imaging. Recent advances in development of MR imaging for fast spatio-temporal cardiac imaging have led to an increased interest in the use of MR imaging for functional analysis of the cardiovascular system. Segmentation of left and right ventricle and modelling of myocardial motion provide important information for quantitative functional analysis. In this talk we show how non-rigid registration techniques can be used to solve these problems in cardiac MR images. We will also demonstrate how similar techniques can be used for the modelling of temporal changes such as growth or atrophy in the brain. Statistical Models of Appearance for Image Analysis ------------------------------------------------------------- Tim Cootes, University of Manchester Statistical models of shape and appearance have been shown to be powerful tools for image interpretation, as they can explicitly deal with the natural variation in objects of interest. Such models can be built from suitably labelled training sets. Given a model of appearance we can match it to a new image using the efficient optimisation algorithms, which seek to minimise the difference between a synthesized model image and the target image. This talk will describe the appoach and recent developments, including new work on automatically finding correspondences across training sets of unlabelled images. Examples will be included from the domains of face interpretation and medical image analysis. _______________________________________________ Informatics & Mathematical Modelling - IMM Technical University of Denmark - DTU Mikkel B. Stegmann M.Sc., Ph.D. Stud. :: +45 4525 3422 :: http://www.imm.dtu.dk/~mbs/ |