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

https://www-sop.inria.fr/asclepios/Publications/Xavier.Pennec/Nicolau.IS4TM03.pdf

Evaluation of a new 3D/2D registration criterion for liver radio-frequencies guided by augmented reality

Recently, several researchers ([BM92], [Zha93], [CM92], [ML92], [CLSB92]) have proposed very interesting methods based on an iterative algorithm to rigidly register surfaces represented by a set of 3d points, when an estimate of the displacement is available. In this paper, we propose to introduce differential informations on points to extend this algorithm. First, we show how to efficiently use curvatures to superpose principal frame at possible corresponding points in order to find the needed rough estimate of the displacement. Then, we explain how to extend this algorithm to look for an affine transformation between two surfaces. We introduce differential informations in points coordinates: this allows us to match locally similar points. We show how this differential information is transformed by an affine transformation. Finally, we introduce curvatures in the best affine transformation criterion and we minimize it using extended Kalman filters. All this extensions are illustrated with experiments on various real biomedical surfaces: teeth, faces, skulls and brains.

https://link.springer.com/content/pdf/10.1007/bfb0028371.pdf

Rigid and affine registration of smooth surfaces using differential properties

https://hal.inria.fr/inria-00615545/document

Medical image tracking

The Expectation Maximization algorithm is a powerful probabilistic tool for brain tissue segmentation. The framework is based on the Gaussian mixture model in MRI, and employs a probabilistic brain atlas as a prior to produce a segmentation of white matter, grey matter and cerebro-spinal fluid (CSF). However, several artifacts can alter the segmentation process. For example, CSF is not a well defined class because of the large quantity of voxels affected by the partial volume effect which alters segmentation results and volume computation. In this study, we show that ignoring vessel segmentation when handling partial volume effect can also lead to false results, more specifically to an over-estimation of the CSF variance in the intensity space. We also propose a more versatile method to improve tissue classification, without a requirement of any outlier class, so that brain tissues, especially the cerebro-spinal fluid, follows the Gaussian noise model in MRI correctly.

/pdf/improved-em-based-tissue-segmentation-and-partial-volume-1dcvrzvk7b.pdf

Improved EM-Based Tissue Segmentation and Partial Volume Effect Quantification in Multi-sequence Brain MRI

We propose a deformable registration algorithm based on unsupervised learning of a low-dimensional probabilistic parameterization of deformations We model registration in a probabilistic and generative fashion, by applying a conditional variational autoencoder (CVAE) network This model enables to also generate normal or pathological deformations of any new image based on the probabilistic latent space Most recent learning-based registration algorithms use supervised labels or deformation models, that miss important properties such as diffeomorphism and sufficiently regular deformation fields In this work, we constrain transformations to be diffeomorphic by using a differentiable exponentiation layer with a symmetric loss function We evaluated our method on 330 cardiac MR sequences and demonstrate robust intra-subject registration results comparable to two state-of-the-art methods but with more regular deformation fields compared to a recent learning-based algorithm Our method reached a mean DICE score of 783% and a mean Hausdorff distance of 79 mm In two preliminary experiments, we illustrate the model’s abilities to transport pathological deformations to healthy subjects and to cluster five diseases in the unsupervised deformation encoding space with a classification performance of 70%

https://hal.inria.fr/hal-01845688/document

Nicholas Ayache

Papers

Evaluation of a new 3D/2D registration criterion for liver radio-frequencies guided by augmented reality

Rigid and affine registration of smooth surfaces using differential properties

Medical image tracking

Improved EM-Based Tissue Segmentation and Partial Volume Effect Quantification in Multi-sequence Brain MRI

Unsupervised Probabilistic Deformation Modeling for Robust Diffeomorphic Registration