다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Advances in functional and structural MR image analysis and implementation as FSL.

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

This paper surveys the field of augmented reality AR, in which 3D virtual objects are integrated into a 3D real environment in real time. It describes the medical, manufacturing, visualization, path planning, entertainment, and military applications that have been explored. This paper describes the characteristics of augmented reality systems, including a detailed discussion of the tradeoffs between optical and video blending approaches. Registration and sensing errors are two of the biggest problems in building effective augmented reality systems, so this paper summarizes current efforts to overcome these problems. Future directions and areas requiring further research are discussed. This survey provides a starting point for anyone interested in researching or using augmented reality.

/pdf/a-survey-of-augmented-reality-17irtaxt7z.pdf

A survey of augmented reality

Geometric matcher for recognizing and positioning 3-D rigid objects from passive stereo

An Augmented Reality System to Guide Liver Punctures

In this paper, we present a method to compute a statistical shape model based on shapes which are represented
by unstructured point sets with arbitrary point numbers. A fundamental problem when computing statistical
shape models is the determination of correspondences between the observations of the associated data set. Often,
homologies between points that represent the surfaces are assumed. When working merely with point clouds, this
might lead to imprecise mean shape and variability results. To overcome this problem, we propose an approach
where exact correspondences are replaced by evolving correspondence probabilities. These are the basis for
a novel algorithm that computes a generative statistical shape model. We developed a unified Maximum A
Posteriori (MAP) framework to compute the model parameters ('mean shape' and 'modes of variation') and the
nuisance parameters which leads to an optimal adaption of the model to the set of observations. The registration
of the model on the observations is solved using the Expectation Maximization - Iterative Closest Point algorithm
which is based on probabilistic correspondences and proved to be robust and fast. The alternated optimization of
the MAP explanation with respect to the observation and the generative model parameters leads to very efficient
and closed-form solutions for nearly all parameters. A comparison with a statistical shape model which is built
using the Iterative Closest Point (ICP) registration algorithm and a Principal Component Analysis (PCA) shows
that our approach leads to better SSM quality measures.

Comparison of statistical shape models built on correspondence probabilities and one-to-one correspondences

Glioblastoma (GBM) is the most common type of primary brain tumor, which is characterized by an infiltrative growth pattern. In current practice, radiotherapy planning is primarily based upon T2 FLAIR MRI despite its known lack of specificity in the detection of tu-mor infiltration. While hyperintensity on T2 FLAIR is widely considered to represent infiltrative tumor, it may also be caused by the presence of vasogenic edema (VE), caused by a leakage of fluid into the brain parenchyma. Distinguishing VE from infiltrative tumor could have im-pact on improving radiotherapy planning. In this paper we study a data set of 17 GBM patients treated with anti-angiogenic therapy for which a fast decrease of T2 FLAIR hypersignal is observed, which indicates the resolution of VE. We investigate if multimodal MRI acquisitions in-cluding diffusion tensor imaging can distinguish between VE and tumor infiltration prior to therapy. Using a random forest classifier, we show that, in this study, morphological information based on the contrast en-hanced T1 image explains up to 75% of the extent of VE. The information from different imaging modalities did not significantly improve the clas-sification. We then show that delineating the VE prior to therapy can have substantial impact on radiotherapy target delineation, leading to smaller treatment volumes and reducing potentially harmful radiation dose to normal brain tissue.

https://hal.inria.fr/hal-01093397/document

Multimodal Analysis of Vasogenic Edema in Glioblastoma Patients for Radiotherapy Planning

We present a novel automated method to segment the my-ocardium of both left and right ventricles in MRI volumes. The segmen-tation is consistent in 3D across the slices such that it can be directly used for mesh generation. Two specific neural networks with multi-scale coarse-to-fine prediction structure are proposed to cope with the small training dataset and trained using an original loss function. The former segments a slice in the middle of the volume. Then the latter iteratively propagates the slice segmentations towards the base and the apex, in a spatially consistent way. We perform 5-fold cross-validation on the 15 cases from STACOM to validate the method. For training, we use real cases and their synthetic variants generated by combining motion simulation and image synthesis. Accurate and consistent testing results are obtained.

Nicholas Ayache

Papers

Geometric matcher for recognizing and positioning 3-D rigid objects from passive stereo

An Augmented Reality System to Guide Liver Punctures

Comparison of statistical shape models built on correspondence probabilities and one-to-one correspondences

Multimodal Analysis of Vasogenic Edema in Glioblastoma Patients for Radiotherapy Planning

3D Consistent Biventricular Myocardial Segmentation Using Deep Learning for Mesh Generation