785 research outputs found
Diffeomorphic Demons using Normalised Mutual Information, Evaluation on Multi-Modal Brain MR Images
The demons algorithm is a fast non-parametric non-rigid registration method. In recent years great efforts have been made to improve the approach; the state of the art version yields symmetric inverse-consistent large-deformation diffeomorphisms. However, only limited work has explored inter-modal similarity metrics, with no practical evaluation on multi-modality data. We present a diffeomorphic demons implementation using the analytical gradient of Normalised Mutual Information (NMI) in a conjugate gradient optimiser. We report the first qualitative and quantitative assessment of the demons for inter-modal registration. Experiments to spatially normalise real MR images, and to recover simulated deformation fields, demonstrate (i) similar accuracy from NMI-demons and classical demons when the latter may be used, and (ii) similar accuracy for NMI-demons on T1w-T1w and T1w-T2w registration, demonstrating its potential in multi-modal scenarios
Diffeomorphic demons using normalized mutual information, evaluation on multimodal brain MR images
The demons algorithm is a fast non-parametric non-rigid registration method. In recent years great efforts have been made to improve the approach; the state of the art version yields symmetric inverse-consistent largedeformation diffeomorphisms. However, only limited work has explored inter-modal similarity metrics, with no practical evaluation on multi-modality data. We present a diffeomorphic demons implementation using the analytical gradient of Normalised Mutual Information (NMI) in a conjugate gradient optimiser. We report the first qualitative and quantitative assessment of the demons for inter-modal registration. Experiments to spatially normalise real MR images, and to recover simulated deformation fields, demonstrate (i) similar accuracy from NMI-demons and classical demons when the latter may be used, and (ii) similar accuracy for NMI-demons on T1w-T1w and T1w-T2w registration, demonstrating its potential in multi-modal scenarios
An unsupervised learning approach to ultrasound strain elastography with spatio-temporal consistency
Quasi-static ultrasound elastography (USE) is an imaging modality that measures deformation (i.e.
strain) of tissue in response to an applied mechanical force. In USE, the strain modulus is traditionally
obtained by deriving the displacement field estimated between a pair of radio-frequency data. In this
work we propose a recurrent network architecture with convolutional long-short-term memory
decoder blocks to improve displacement estimation and spatio-temporal continuity between time
series ultrasound frames. The network is trained in an unsupervised way, by optimising a similarity
metric between the reference and compressed image. Our training loss is also composed of a
regularisation term that preserves displacement continuity by directly optimising the strain
smoothness, and a temporal continuity term that enforces consistency between successive strain
predictions. In addition, we propose an open-access in vivo database for quasi-static USE, which
consists of radio-frequency data sequences captured on the arm of a human volunteer. Our results
from numerical simulation and in vivo data suggest that our recurrent neural network can account for
larger deformations, as compared with two other feed-forward neural networks. In all experiments,
our recurrent network outperformed the state-of-the-art for both learning-based and optimisationbased
methods, in terms of elastographic signal-to-noise ratio, strain consistency, and image
similarity. Finally, our open-source code provides a 3D-slicer visualisation module that can be used to
process ultrasound RF frames in real-time, at a rate of up to 20 frames per second, using a
standard GPU
An Unsupervised Approach to Ultrasound Elastography with End-to-end Strain Regularisation
Quasi-static ultrasound elastography (USE) is an imaging modality that consists of determining a measure of deformation (i.e. strain) of soft tissue in response to an applied mechanical force. The strain is generally determined by estimating the displacement between successive ultrasound frames acquired before and after applying manual compression. The computational efficiency and accuracy of the displacement prediction, also known as time-delay estimation, are key challenges for real-time USE applications. In this paper, we present a novel deep-learning method for efficient time-delay estimation between ultrasound radio-frequency (RF) data. The proposed method consists of a convolutional neural network (CNN) that predicts a displacement field between a pair of pre- and post-compression ultrasound RF frames. The network is trained in an unsupervised way, by optimizing a similarity metric between the reference and compressed image. We also introduce a new regularization term that preserves displacement continuity by directly optimizing the strain smoothness. We validated the performance of our method by using both ultrasound simulation and in vivo data on healthy volunteers. We also compared the performance of our method with a state-of-the-art method called OVERWIND [17]. Average contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of our method in 30 simulation and 3 in vivo image pairs are 7.70 and 6.95, 7 and 0.31, respectively. Our results suggest that our approach can effectively predict accurate strain images. The unsupervised aspect of our approach represents a great potential for the use of deep learning application for the analysis of clinical ultrasound data
An unsupervised learning-based shear wave tracking method for ultrasound elastography
Shear wave elastography involves applying a non-invasive acoustic radiation force to the tissue and imaging the induced deformation to infer its mechanical properties. This work investigates the use of convolutional neural networks to improve displacement estimation accuracy in shear wave imaging. Our training approach is completely unsupervised, which allows to learn the estimation of the induced micro-scale deformations without ground truth labels. We also present an ultrasound simulation dataset where the shear wave propagation has been simulated via finite element method. Our dataset is made publicly available along with this paper, and consists in 150 shear wave propagation simulations in both homogenous and hetegeneous media, which represents a total of 20,000 ultrasound images. We assessed the ability of our learning-based approach to characterise tissue elastic properties (i.e., Young's modulus) on our dataset and compared our results with a classical normalised cross-correlation approach
Predicting spatial spread of rabies in skunk populations using surveillance data reported by the public
Background:
Prevention and control of wildlife disease invasions relies on the ability to predict spatio-temporal dynamics and understand the role of factors driving spread rates, such as seasonality and transmission distance. Passive disease surveillance (i.e., case reports by public) is a common method of monitoring emergence of wildlife diseases, but can be challenging to interpret due to spatial biases and limitations in data quantity and quality.
Methodology/Principal findings:
We obtained passive rabies surveillance data from dead striped skunks (Mephitis mephitis) in an epizootic in northern Colorado, USA. We developed a dynamic patch-occupancy model which predicts spatio-temporal spreading while accounting for heterogeneous sampling. We estimated the distance travelled per transmission event, direction of invasion, rate of spatial spread, and effects of infection density and season. We also estimated mean transmission distance and rates of spatial spread using a phylogeographic approach on a subsample of viral sequences from the same epizootic. Both the occupancy and phylogeographic approaches predicted similar rates of spatio-temporal spread. Estimated mean transmission distances were 2.3 km (95% Highest Posterior Density (HPD95): 0.02, 11.9; phylogeographic) and 3.9 km (95% credible intervals (CI95): 1.4, 11.3; occupancy). Estimated rates of spatial spread in km/year were: 29.8 (HPD95: 20.8, 39.8; phylogeographic, branch velocity, homogenous model), 22.6 (HPD95: 15.3, 29.7; phylogeographic, diffusion rate, homogenous model) and 21.1 (CI95: 16.7, 25.5; occupancy). Initial colonization probability was twice as high in spring relative to fall.
Conclusions/Significance:
Skunk-to-skunk transmission was primarily local (< 4 km) suggesting that if interventions were needed, they could be applied at the wave front. Slower viral invasions of skunk rabies in western USA compared to a similar epizootic in raccoons in the eastern USA implies host species or landscape factors underlie the dynamics of rabies invasions. Our framework provides a straightforward method for estimating rates of spatial spread of wildlife diseases
Drie nieuwe Ponto-Kaspische inwijkelingen dringen door tot in kanalen in de provincie Antwerpen: De zoetwaterpolychaet <i>Hypania invalida</i> (Grube, 1860) en, voor het eerst in België, de platworm <i>Dendrocoelum romanodanubiale</i> (Codreanu, 1949) en de Donaupissebed <i>Jaera istri</i> Veuille, 1979
Since 2000 three Ponto-Caspian invaders have been found in canals in the province of Antwerp (N.E.-Belgium): the freshwater polychaete Hypania invalida (Grube, 1860), the triclad Dendrocoelum romanodanubiale (Codreanu, 1949) and the freshwater isopod Jaera istri Veuille, 1979. Staff members of the 'Vlaamse Milieumaatschappij (Flemish Environmental Agency) collected these species on artificial substrates (nets filled with broken bricks). H. invalida had been discovered before in Belgium in the river Meuse in 2000 (Vanden Bossche et.al., 2001). D. romanodanubiale and J. istri were never recorded before in Belgium. Before its discovery in the Belgian Meuse in August-September 2000, H.invalida had already been found in May 2000 in the Albert Canal at Genk in the province of Limbourg. Between 2001 and 2003, the polychaete was sampled at several stations of the Albert Canal and its adjacent canals in the provinces of Antwerp and Limbourg. Here, it often is accompanied by D. romanodanubiale and J. istri. In 2003 the polychaet had also been encountered in the Sea canal Brussels - Scheldt in the province of Antwerp. The discontinuous distribution indicates that navigation plays an important role in the dispersal of the species. Other Ponto-Caspian species such as Dikerogammarus villosus (Sovinskij, 1894) are either already present in the canals or soon to be expected there. Some of these species will probably also invade the Scheldt basin
Forward-Backward Splitting in Deformable Image Registration: A Demons Approach
Efficient non-linear image registration implementations are
key for many biomedical imaging applications. By using the
classical demons approach, the associated optimization problem
is solved by an alternate optimization scheme consisting
of a gradient descent step followed by Gaussian smoothing.
Despite being simple and powerful, the solution of the underlying
relaxed formulation is not guaranteed to minimize
the original global energy. Implicitly, however, this second
step can be recast as the proximal map of the regularizer.
This interpretation introduces a parallel to the more general
Forward-Backward Splitting (FBS) scheme consisting of a
forward gradient descent and proximal step. By shifting entirely
to FBS, we can take advantage of the recent advances in
FBS methods and solve the original, non-relaxed deformable
registration problem for any type of differentiable similarity
measure and convex regularization associated with a tractable
proximal operator. Additionally, global convergence to a
critical point is guaranteed under weak restrictions. For the
first time in the context of image registration, we show that
Tikhonov regularization breaks down to the simple use of
B-Spline filtering in the proximal step. We demonstrate the
versatility of FBS by encoding spatial transformation as displacement
fields or free-form B-Spline deformations. We use
state-of-the-art FBS solvers and compare their performance
against the classical demons, the recently proposed inertial
demons and the conjugate gradient optimizer. Numerical experiments
performed on both synthetic and clinical data show
the advantage of FBS in image registration in terms of both
convergence and accuracy
- …