424 research outputs found
Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow
We propose a method to classify cardiac pathology based on a novel approach
to extract image derived features to characterize the shape and motion of the
heart. An original semi-supervised learning procedure, which makes efficient
use of a large amount of non-segmented images and a small amount of images
segmented manually by experts, is developed to generate pixel-wise apparent
flow between two time points of a 2D+t cine MRI image sequence. Combining the
apparent flow maps and cardiac segmentation masks, we obtain a local apparent
flow corresponding to the 2D motion of myocardium and ventricular cavities.
This leads to the generation of time series of the radius and thickness of
myocardial segments to represent cardiac motion. These time series of motion
features are reliable and explainable characteristics of pathological cardiac
motion. Furthermore, they are combined with shape-related features to classify
cardiac pathologies. Using only nine feature values as input, we propose an
explainable, simple and flexible model for pathology classification. On ACDC
training set and testing set, the model achieves 95% and 94% respectively as
classification accuracy. Its performance is hence comparable to that of the
state-of-the-art. Comparison with various other models is performed to outline
some advantages of our model
3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context
We present an efficient deep learning approach for the challenging task of
tumor segmentation in multisequence MR images. In recent years, Convolutional
Neural Networks (CNN) have achieved state-of-the-art performances in a large
variety of recognition tasks in medical imaging. Because of the considerable
computational cost of CNNs, large volumes such as MRI are typically processed
by subvolumes, for instance slices (axial, coronal, sagittal) or small 3D
patches. In this paper we introduce a CNN-based model which efficiently
combines the advantages of the short-range 3D context and the long-range 2D
context. To overcome the limitations of specific choices of neural network
architectures, we also propose to merge outputs of several cascaded 2D-3D
models by a voxelwise voting strategy. Furthermore, we propose a network
architecture in which the different MR sequences are processed by separate
subnetworks in order to be more robust to the problem of missing MR sequences.
Finally, a simple and efficient algorithm for training large CNN models is
introduced. We evaluate our method on the public benchmark of the BRATS 2017
challenge on the task of multiclass segmentation of malignant brain tumors. Our
method achieves good performances and produces accurate segmentations with
median Dice scores of 0.918 (whole tumor), 0.883 (tumor core) and 0.854
(enhancing core). Our approach can be naturally applied to various tasks
involving segmentation of lesions or organs.Comment: Submitted to the journal Computerized Medical Imaging and Graphic
Quasi-symplectic Langevin Variational Autoencoder
Variational autoencoder (VAE) is a very popular and well-investigated
generative model vastly used in neural learning research. To leverage VAE in
practical tasks dealing with a massive dataset of large dimensions it is
required to deal with the difficulty of building low variance evidence lower
bounds (ELBO). Markov ChainMonte Carlo (MCMC) is one of the effective
approaches to tighten the ELBO for approximating the posterior distribution.
Hamiltonian Variational Autoencoder(HVAE) is an effective MCMC inspired
approach for constructing a low-variance ELBO which is also amenable to the
reparameterization trick. In this work, we propose a Quasi-symplectic Langevin
Variational autoencoder (Langevin-VAE) by incorporating the gradients
information in the inference process through the Langevin dynamic. We show the
effectiveness of the proposed approach by toy and real-world examples
A model of brain morphological changes related to aging and Alzheimer's disease from cross-sectional assessments
In this study we propose a deformation-based framework to jointly model the
influence of aging and Alzheimer's disease (AD) on the brain morphological
evolution. Our approach combines a spatio-temporal description of both
processes into a generative model. A reference morphology is deformed along
specific trajectories to match subject specific morphologies. It is used to
define two imaging progression markers: 1) a morphological age and 2) a disease
score. These markers can be computed locally in any brain region. The approach
is evaluated on brain structural magnetic resonance images (MRI) from the ADNI
database. The generative model is first estimated on a control population,
then, for each subject, the markers are computed for each acquisition. The
longitudinal evolution of these markers is then studied in relation with the
clinical diagnosis of the subjects and used to generate possible morphological
evolution. In the model, the morphological changes associated with normal aging
are mainly found around the ventricles, while the Alzheimer's disease specific
changes are more located in the temporal lobe and the hippocampal area. The
statistical analysis of these markers highlights differences between clinical
conditions even though the inter-subject variability is quiet high. In this
context, the model can be used to generate plausible morphological trajectories
associated with the disease. Our method gives two interpretable scalar imaging
biomarkers assessing the effects of aging and disease on brain morphology at
the individual and population level. These markers confirm an acceleration of
apparent aging for Alzheimer's subjects and can help discriminate clinical
conditions even in prodromal stages. More generally, the joint modeling of
normal and pathological evolutions shows promising results to describe
age-related brain diseases over long time scales.Comment: NeuroImage, Elsevier, In pres
De l'imagerie médicale à la modélisation numérique personnalisée du corps humain
National audienceL'imagerie médicale fournit des informations trÚs riches sur l'anatomie et la physiologie d'un patient. L'analyse par ordinateur de ces images permet d'extraire des quantités géométriques, cinématiques ou fonctionnelles. Ces grandeurs peuvent servir à personnaliser des modÚles computationnels du corps humain afin qu'ils soient spécifiques à un patient donné. Trois exemples de tels modÚles personnalisés sont décrits par la suite
General Object Reconstruction based on Simplex Meshes
In this paper, we propose a general tridimensional reconstruction algorithm of range and volumetric images, based on deformable simplex meshes. The algorithm is able to reconstruct surfaces without any restriction on their shape or topology. The different tasks performed during the reconstruction include the segmentation of objects in the scene, the extrapolation of missing data and the control of smoothness, density and geometric quality of the reconstructed model. All surfaces are represented as simplex meshes, that are unstructured meshes whose topology is dual of triangulations. The reconstruction takes place in two stages. First, we initialize the model either manually or using an automatic initialization routine. After the first fit, the topology of the model can be modified by creating holes or increasing its genus. Finally, an iterative adaptation or refinement algorithm decrease the distance of the model from the data while preserving a high geometric and topological quality. We have applied our algorithm to several medical images or range images
Haptic Rendering of Hyperelastic Models with Friction
International audienceâ This paper presents an original method for inter-actions' haptic rendering when treating hyperelastic materials. Such simulations are known to be difficult due to the non-linear behavior of hyperelastic bodies; furthermore, haptic constraints enjoin contact forces to be refreshed at least at 1000 updates per second. To enforce the stability of simulations of generic objects of any range of stiffness, this method relies on implicit time integration. Soft tissues dynamics is simulated in real time (20 to 100 Hz) using the Multiplicative Jacobian Energy Decomposition (MJED) method. An asynchronous preconditioner, updated at low rates (1 to 10 Hz), is used to obtain a close approximation of the mechanical coupling of interactions. Finally, the contact problem is linearized and, using a specific-loop, it is updated at typical haptic rates (around 1000 Hz) allowing this way new simulations of prompt stiff-contacts and providing a continuous haptic feedback as well
3D Consistent Biventricular Myocardial Segmentation Using Deep Learning for Mesh Generation
We present a novel automated method to segment the myocardium of both left
and right ventricles in MRI volumes. The segmentation 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
Volumetric Medical Images Segmentation using Shape Constrained Deformable Models
International audienceIn this paper we address the problem of extracting geometric models from low contrast volumetric images, given a template or reference shape of that model. We proceed by deforming a reference model in a volumetric image. This reference deformable model is represented as a simplex mesh submitted to regularizing shape constraint. Furthermore, we introduce an original approach that combines the deformable model framework with the elastic registration (based on iterative closest point algorithm) method. This new method increases the robustness of segmentation while allowing very complex deformation of the original template. Examples of segmentation of the liver and brain ventricles are provided
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