2,780 research outputs found
Nonparametric joint shape learning for customized shape modeling
We present a shape optimization approach to compute patient-specific models in customized prototyping applications. We design a coupled shape prior to model the transformation between a related pair of surfaces, using a nonparametric joint probability density estimation. The coupled shape prior forces with the help of application-specific data forces and smoothness forces drive a surface deformation
towards a desired output surface. We demonstrate the usefulness of the method for generating customized shape models in applications of hearing aid design and pre-operative to intra-operative anatomic surface estimation
Segmentation of skin lesions in 2D and 3D ultrasound images using a spatially coherent generalized Rayleigh mixture model
This paper addresses the problem of jointly estimating the statistical distribution and segmenting lesions in multiple-tissue high-frequency skin ultrasound images. The distribution of multiple-tissue images is modeled as a spatially coherent finite mixture of heavy-tailed Rayleigh distributions. Spatial coherence inherent to biological tissues is modeled by enforcing local dependence between the mixture components. An original Bayesian algorithm combined with a Markov chain Monte Carlo method is then proposed to jointly estimate the mixture parameters and a label-vector associating each voxel to a tissue. More precisely, a hybrid Metropolis-within-Gibbs sampler is used to draw samples that are asymptotically distributed according to the posterior distribution of the Bayesian model. The Bayesian estimators of the model parameters are then computed from the generated samples. Simulation results are conducted on synthetic data to illustrate the performance of the proposed estimation strategy. The method is then successfully applied to the segmentation of in vivo skin tumors in high-frequency 2-D and 3-D ultrasound images
Mixture of Kernels and Iterated Semidirect Product of Diffeomorphisms Groups
In the framework of large deformation diffeomorphic metric mapping (LDDMM),
we develop a multi-scale theory for the diffeomorphism group based on previous
works. The purpose of the paper is (1) to develop in details a variational
approach for multi-scale analysis of diffeomorphisms, (2) to generalise to
several scales the semidirect product representation and (3) to illustrate the
resulting diffeomorphic decomposition on synthetic and real images. We also
show that the approaches presented in other papers and the mixture of kernels
are equivalent.Comment: 21 pages, revised version without section on evaluatio
Classical and all-floating FETI methods for the simulation of arterial tissues
High-resolution and anatomically realistic computer models of biological soft
tissues play a significant role in the understanding of the function of
cardiovascular components in health and disease. However, the computational
effort to handle fine grids to resolve the geometries as well as sophisticated
tissue models is very challenging. One possibility to derive a strongly
scalable parallel solution algorithm is to consider finite element tearing and
interconnecting (FETI) methods. In this study we propose and investigate the
application of FETI methods to simulate the elastic behavior of biological soft
tissues. As one particular example we choose the artery which is - as most
other biological tissues - characterized by anisotropic and nonlinear material
properties. We compare two specific approaches of FETI methods, classical and
all-floating, and investigate the numerical behavior of different
preconditioning techniques. In comparison to classical FETI, the all-floating
approach has not only advantages concerning the implementation but in many
cases also concerning the convergence of the global iterative solution method.
This behavior is illustrated with numerical examples. We present results of
linear elastic simulations to show convergence rates, as expected from the
theory, and results from the more sophisticated nonlinear case where we apply a
well-known anisotropic model to the realistic geometry of an artery. Although
the FETI methods have a great applicability on artery simulations we will also
discuss some limitations concerning the dependence on material parameters.Comment: 29 page
Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy
In recent years, endomicroscopy has become increasingly used for diagnostic
purposes and interventional guidance. It can provide intraoperative aids for
real-time tissue characterization and can help to perform visual investigations
aimed for example to discover epithelial cancers. Due to physical constraints
on the acquisition process, endomicroscopy images, still today have a low
number of informative pixels which hampers their quality. Post-processing
techniques, such as Super-Resolution (SR), are a potential solution to increase
the quality of these images. SR techniques are often supervised, requiring
aligned pairs of low-resolution (LR) and high-resolution (HR) images patches to
train a model. However, in our domain, the lack of HR images hinders the
collection of such pairs and makes supervised training unsuitable. For this
reason, we propose an unsupervised SR framework based on an adversarial deep
neural network with a physically-inspired cycle consistency, designed to impose
some acquisition properties on the super-resolved images. Our framework can
exploit HR images, regardless of the domain where they are coming from, to
transfer the quality of the HR images to the initial LR images. This property
can be particularly useful in all situations where pairs of LR/HR are not
available during the training. Our quantitative analysis, validated using a
database of 238 endomicroscopy video sequences from 143 patients, shows the
ability of the pipeline to produce convincing super-resolved images. A Mean
Opinion Score (MOS) study also confirms this quantitative image quality
assessment.Comment: Accepted for publication on Medical Image Analysis journa
Retrospective correction of Rigid and Non-Rigid MR motion artifacts using GANs
Motion artifacts are a primary source of magnetic resonance (MR) image
quality deterioration with strong repercussions on diagnostic performance.
Currently, MR motion correction is carried out either prospectively, with the
help of motion tracking systems, or retrospectively by mainly utilizing
computationally expensive iterative algorithms. In this paper, we utilize a new
adversarial framework, titled MedGAN, for the joint retrospective correction of
rigid and non-rigid motion artifacts in different body regions and without the
need for a reference image. MedGAN utilizes a unique combination of
non-adversarial losses and a new generator architecture to capture the textures
and fine-detailed structures of the desired artifact-free MR images.
Quantitative and qualitative comparisons with other adversarial techniques have
illustrated the proposed model performance.Comment: 5 pages, 2 figures, under review for the IEEE International Symposium
for Biomedical Image
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