9,284 research outputs found
Gaussian Process Morphable Models
Statistical shape models (SSMs) represent a class of shapes as a normal
distribution of point variations, whose parameters are estimated from example
shapes. Principal component analysis (PCA) is applied to obtain a
low-dimensional representation of the shape variation in terms of the leading
principal components. In this paper, we propose a generalization of SSMs,
called Gaussian Process Morphable Models (GPMMs). We model the shape variations
with a Gaussian process, which we represent using the leading components of its
Karhunen-Loeve expansion. To compute the expansion, we make use of an
approximation scheme based on the Nystrom method. The resulting model can be
seen as a continuous analogon of an SSM. However, while for SSMs the shape
variation is restricted to the span of the example data, with GPMMs we can
define the shape variation using any Gaussian process. For example, we can
build shape models that correspond to classical spline models, and thus do not
require any example data. Furthermore, Gaussian processes make it possible to
combine different models. For example, an SSM can be extended with a spline
model, to obtain a model that incorporates learned shape characteristics, but
is flexible enough to explain shapes that cannot be represented by the SSM. We
introduce a simple algorithm for fitting a GPMM to a surface or image. This
results in a non-rigid registration approach, whose regularization properties
are defined by a GPMM. We show how we can obtain different registration
schemes,including methods for multi-scale, spatially-varying or hybrid
registration, by constructing an appropriate GPMM. As our approach strictly
separates modelling from the fitting process, this is all achieved without
changes to the fitting algorithm. We show the applicability and versatility of
GPMMs on a clinical use case, where the goal is the model-based segmentation of
3D forearm images
Finite Element Based Tracking of Deforming Surfaces
We present an approach to robustly track the geometry of an object that
deforms over time from a set of input point clouds captured from a single
viewpoint. The deformations we consider are caused by applying forces to known
locations on the object's surface. Our method combines the use of prior
information on the geometry of the object modeled by a smooth template and the
use of a linear finite element method to predict the deformation. This allows
the accurate reconstruction of both the observed and the unobserved sides of
the object. We present tracking results for noisy low-quality point clouds
acquired by either a stereo camera or a depth camera, and simulations with
point clouds corrupted by different error terms. We show that our method is
also applicable to large non-linear deformations.Comment: additional experiment
Robust Cardiac Motion Estimation using Ultrafast Ultrasound Data: A Low-Rank-Topology-Preserving Approach
Cardiac motion estimation is an important diagnostic tool to detect heart
diseases and it has been explored with modalities such as MRI and conventional
ultrasound (US) sequences. US cardiac motion estimation still presents
challenges because of the complex motion patterns and the presence of noise. In
this work, we propose a novel approach to estimate the cardiac motion using
ultrafast ultrasound data. -- Our solution is based on a variational
formulation characterized by the L2-regularized class. The displacement is
represented by a lattice of b-splines and we ensure robustness by applying a
maximum likelihood type estimator. While this is an important part of our
solution, the main highlight of this paper is to combine a low-rank data
representation with topology preservation. Low-rank data representation
(achieved by finding the k-dominant singular values of a Casorati Matrix
arranged from the data sequence) speeds up the global solution and achieves
noise reduction. On the other hand, topology preservation (achieved by
monitoring the Jacobian determinant) allows to radically rule out distortions
while carefully controlling the size of allowed expansions and contractions.
Our variational approach is carried out on a realistic dataset as well as on a
simulated one. We demonstrate how our proposed variational solution deals with
complex deformations through careful numerical experiments. While maintaining
the accuracy of the solution, the low-rank preprocessing is shown to speed up
the convergence of the variational problem. Beyond cardiac motion estimation,
our approach is promising for the analysis of other organs that experience
motion.Comment: 15 pages, 10 figures, Physics in Medicine and Biology, 201
Medical image computing and computer-aided medical interventions applied to soft tissues. Work in progress in urology
Until recently, Computer-Aided Medical Interventions (CAMI) and Medical
Robotics have focused on rigid and non deformable anatomical structures.
Nowadays, special attention is paid to soft tissues, raising complex issues due
to their mobility and deformation. Mini-invasive digestive surgery was probably
one of the first fields where soft tissues were handled through the development
of simulators, tracking of anatomical structures and specific assistance
robots. However, other clinical domains, for instance urology, are concerned.
Indeed, laparoscopic surgery, new tumour destruction techniques (e.g. HIFU,
radiofrequency, or cryoablation), increasingly early detection of cancer, and
use of interventional and diagnostic imaging modalities, recently opened new
challenges to the urologist and scientists involved in CAMI. This resulted in
the last five years in a very significant increase of research and developments
of computer-aided urology systems. In this paper, we propose a description of
the main problems related to computer-aided diagnostic and therapy of soft
tissues and give a survey of the different types of assistance offered to the
urologist: robotization, image fusion, surgical navigation. Both research
projects and operational industrial systems are discussed
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