2,462 research outputs found
Quicksilver: Fast Predictive Image Registration - a Deep Learning Approach
This paper introduces Quicksilver, a fast deformable image registration
method. Quicksilver registration for image-pairs works by patch-wise prediction
of a deformation model based directly on image appearance. A deep
encoder-decoder network is used as the prediction model. While the prediction
strategy is general, we focus on predictions for the Large Deformation
Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the
momentum-parameterization of LDDMM, which facilitates a patch-wise prediction
strategy while maintaining the theoretical properties of LDDMM, such as
guaranteed diffeomorphic mappings for sufficiently strong regularization. We
also provide a probabilistic version of our prediction network which can be
sampled during the testing time to calculate uncertainties in the predicted
deformations. Finally, we introduce a new correction network which greatly
increases the prediction accuracy of an already existing prediction network. We
show experimental results for uni-modal atlas-to-image as well as uni- / multi-
modal image-to-image registrations. These experiments demonstrate that our
method accurately predicts registrations obtained by numerical optimization, is
very fast, achieves state-of-the-art registration results on four standard
validation datasets, and can jointly learn an image similarity measure.
Quicksilver is freely available as an open-source software.Comment: Add new discussion
Advanced Algorithms for 3D Medical Image Data Fusion in Specific Medical Problems
FĂşze obrazu je dnes jednou z nejbÄĹžnÄjĹĄĂch avĹĄak stĂĄle velmi diskutovanou oblastĂ v lĂŠkaĹskĂŠm zobrazovĂĄnĂ a hraje dĹŻleĹžitou roli ve vĹĄech oblastech lĂŠkaĹskĂŠ pĂŠÄe jako je diagnĂłza, lĂŠÄba a chirurgie. V tĂŠto dizertaÄnĂ prĂĄci jsou pĹedstaveny tĹi projekty, kterĂŠ jsou velmi Ăşzce spojeny s oblastĂ fĂşze medicĂnskĂ˝ch dat. PrvnĂ projekt pojednĂĄvĂĄ o 3D CT subtrakÄnĂ angiografii dolnĂch konÄetin. V prĂĄci je vyuĹžito kombinace kontrastnĂch a nekontrastnĂch dat pro zĂskĂĄnĂ kompletnĂho cĂŠvnĂho stromu. DruhĂ˝ projekt se zabĂ˝vĂĄ fĂşzĂ DTI a T1 vĂĄhovanĂ˝ch MRI dat mozku. CĂlem tohoto projektu je zkombinovat stukturĂĄlnĂ a funkÄnĂ informace, kterĂŠ umoĹžĹujĂ zlepĹĄit znalosti konektivity v mozkovĂŠ tkĂĄni. TĹetĂ projekt se zabĂ˝vĂĄ metastĂĄzemi v CT ÄasovĂ˝ch datech pĂĄteĹe. Tento projekt je zamÄĹen na studium vĂ˝voje metastĂĄz uvnitĹ obratlĹŻ ve fĂşzovanĂŠ ÄasovĂŠ ĹadÄ snĂmkĹŻ. Tato dizertaÄnĂ prĂĄce pĹedstavuje novou metodologii pro klasifikaci tÄchto metastĂĄz. VĹĄechny projekty zmĂnÄnĂŠ v tĂŠto dizertaÄnĂ prĂĄci byly ĹeĹĄeny v rĂĄmci pracovnĂ skupiny zabĂ˝vajĂcĂ se analĂ˝zou lĂŠkaĹskĂ˝ch dat, kterou vedl pan Prof. JiĹĂ Jan. Tato dizertaÄnĂ prĂĄce obsahuje registraÄnĂ ÄĂĄst prvnĂho a klasifikaÄnĂ ÄĂĄst tĹetĂho projektu. DruhĂ˝ projekt je pĹedstaven kompletnÄ. DalĹĄĂ ÄĂĄst prvnĂho a tĹetĂho projektu, obsahujĂcĂ specifickĂŠ pĹedzpracovĂĄnĂ dat, jsou obsaĹženy v disertaÄnĂ prĂĄci mĂŠho kolegy Ing. Romana Petera.Image fusion is one of today´s most common and still challenging tasks in medical imaging and it plays crucial role in all areas of medical care such as diagnosis, treatment and surgery. Three projects crucially dependent on image fusion are introduced in this thesis. The first project deals with the 3D CT subtraction angiography of lower limbs. It combines pre-contrast and contrast enhanced data to extract the blood vessel tree. The second project fuses the DTI and T1-weighted MRI brain data. The aim of this project is to combine the brain structural and functional information that purvey improved knowledge about intrinsic brain connectivity. The third project deals with the time series of CT spine data where the metastases occur. In this project the progression of metastases within the vertebrae is studied based on fusion of the successive elements of the image series. This thesis introduces new methodology of classifying metastatic tissue. All the projects mentioned in this thesis have been solved by the medical image analysis group led by Prof. JiĹĂ Jan. This dissertation concerns primarily the registration part of the first project and the classification part of the third project. The second project is described completely. The other parts of the first and third project, including the specific preprocessing of the data, are introduced in detail in the dissertation thesis of my colleague Roman Peter, M.Sc.
A skeletonization algorithm for gradient-based optimization
The skeleton of a digital image is a compact representation of its topology,
geometry, and scale. It has utility in many computer vision applications, such
as image description, segmentation, and registration. However, skeletonization
has only seen limited use in contemporary deep learning solutions. Most
existing skeletonization algorithms are not differentiable, making it
impossible to integrate them with gradient-based optimization. Compatible
algorithms based on morphological operations and neural networks have been
proposed, but their results often deviate from the geometry and topology of the
true medial axis. This work introduces the first three-dimensional
skeletonization algorithm that is both compatible with gradient-based
optimization and preserves an object's topology. Our method is exclusively
based on matrix additions and multiplications, convolutional operations, basic
non-linear functions, and sampling from a uniform probability distribution,
allowing it to be easily implemented in any major deep learning library. In
benchmarking experiments, we prove the advantages of our skeletonization
algorithm compared to non-differentiable, morphological, and
neural-network-based baselines. Finally, we demonstrate the utility of our
algorithm by integrating it with two medical image processing applications that
use gradient-based optimization: deep-learning-based blood vessel segmentation,
and multimodal registration of the mandible in computed tomography and magnetic
resonance images.Comment: Accepted at ICCV 202
Learning the temporal evolution of multivariate densities via normalizing flows
In this work, we propose a method to learn multivariate probability
distributions using sample path data from stochastic differential equations.
Specifically, we consider temporally evolving probability distributions (e.g.,
those produced by integrating local or nonlocal Fokker-Planck equations). We
analyze this evolution through machine learning assisted construction of a
time-dependent mapping that takes a reference distribution (say, a Gaussian) to
each and every instance of our evolving distribution. If the reference
distribution is the initial condition of a Fokker-Planck equation, what we
learn is the time-T map of the corresponding solution. Specifically, the
learned map is a multivariate normalizing flow that deforms the support of the
reference density to the support of each and every density snapshot in time. We
demonstrate that this approach can approximate probability density function
evolutions in time from observed sampled data for systems driven by both
Brownian and L\'evy noise. We present examples with two- and three-dimensional,
uni- and multimodal distributions to validate the method
Deep learning cardiac motion analysis for human survival prediction
Motion analysis is used in computer vision to understand the behaviour of
moving objects in sequences of images. Optimising the interpretation of dynamic
biological systems requires accurate and precise motion tracking as well as
efficient representations of high-dimensional motion trajectories so that these
can be used for prediction tasks. Here we use image sequences of the heart,
acquired using cardiac magnetic resonance imaging, to create time-resolved
three-dimensional segmentations using a fully convolutional network trained on
anatomical shape priors. This dense motion model formed the input to a
supervised denoising autoencoder (4Dsurvival), which is a hybrid network
consisting of an autoencoder that learns a task-specific latent code
representation trained on observed outcome data, yielding a latent
representation optimised for survival prediction. To handle right-censored
survival outcomes, our network used a Cox partial likelihood loss function. In
a study of 302 patients the predictive accuracy (quantified by Harrell's
C-index) was significantly higher (p < .0001) for our model C=0.73 (95 CI:
0.68 - 0.78) than the human benchmark of C=0.59 (95 CI: 0.53 - 0.65). This
work demonstrates how a complex computer vision task using high-dimensional
medical image data can efficiently predict human survival
Finite element surface registration incorporating curvature, volume preservation, and statistical model information
We present a novel method for nonrigid registration of 3D surfaces and images. The method can be used to register surfaces by means of their distance images, or to register medical images directly. It is formulated as a minimization problem of a sum of several terms representing the desired properties of a registration result: smoothness, volume preservation, matching of the surface, its curvature, and possible other feature images, as well as consistency with previous registration results of similar objects, represented by a statistical deformation model. While most of these concepts are already known, we present a coherent continuous formulation of these constraints, including the statistical deformation model. This continuous formulation renders the registration method independent of its discretization. The finite element discretization we present is, while independent of the registration functional, the second main contribution of this paper. The local discontinuous Galerkin method has not previously been used in image registration, and it provides an efficient and general framework to discretize each of the terms of our functional. Computational efficiency and modest memory consumption are achieved thanks to parallelization and locally adaptive mesh refinement. This allows for the first time the use of otherwise prohibitively large 3D statistical deformation models
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