116 research outputs found
Coupled nonparametric shape priors for segmentation of multiple basal ganglia structures
This paper presents a new method for multiple structure segmentation,
using a maximum a posteriori (MAP) estimation framework,
based on prior shape densities involving nonparametric multivariate
kernel density estimation of multiple shapes. Our method is motivated
by the observation that neighboring or coupling structures
in medical images generate configurations and co-dependencies
which could potentially aid in segmentation if properly exploited.
Our technique allows simultaneous segmentation of multiple objects,
where highly contrasted, easy-to-segment structures can help
improve the segmentation of weakly contrasted objects. We demonstrate
the effectiveness of our method on both synthetic images and
real magnetic resonance images (MRI) for segmentation of basal
ganglia structures
Volumetric segmentation of multiple basal ganglia structures
We present a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. Neighboring anatomical structures in the human brain exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models
on the shapes and inter-shape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities based on training data, we use a nonparametric multivariate kernel density estimation framework.
We combine these priors with data in a variational framework, and develop an active contour-based iterative segmentation algorithm. We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance (MR) images. We compare our technique with existing methods and demonstrate the improvements it provides in terms of segmentation accuracy
Coupled non-parametric shape and moment-based inter-shape pose priors for multiple basal ganglia structure segmentation
This paper presents a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. In biological tissues, such as the human brain, neighboring structures exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and inter-shape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework and develop an active contour-based iterative segmentation algorithm.
We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance (MR) images.
We present a set of 2D and 3D experiments as well as a quantitative performance analysis. In addition, we perform a comparison to several existent segmentation methods and demonstrate the improvements provided by our approach in terms of segmentation accuracy
Multi-object segmentation using coupled nonparametric shape and relative pose priors
We present a new method for multi-object segmentation in a maximum a posteriori estimation framework. Our method is motivated by the observation that neighboring or coupling objects in images generate configurations and co-dependencies which could potentially aid in segmentation if properly exploited. Our approach employs coupled shape and inter-shape pose priors that are computed using training images in a nonparametric multi-variate kernel density estimation framework. The coupled shape prior is obtained by estimating the joint shape distribution of multiple objects and the inter-shape pose priors are modeled via standard moments. Based on such statistical models, we formulate an optimization problem for segmentation, which we solve by an algorithm based on active contours. Our technique provides significant improvements in the segmentation of weakly contrasted objects in a number of applications. In particular for medical image analysis, we use our method to extract brain Basal Ganglia structures, which are members of a complex multi-object system posing a challenging segmentation problem. We also apply our technique to the problem of handwritten character segmentation. Finally, we use our method to segment cars in urban scenes
Ăoklu beyin yapılarının baÄlaĆık, parametrik olmayan Ćekil önbilgisi kullanılarak bölĂŒtlenmesi = Segmentation of multiple brain structures using coupled nonparametric shape priors
We consider the problem of segmenting multiple brain
structures in medical images. Due to the low contrast of
medical images and the presence of noise, solution of this
problem based only on image data, is rather challenging.
Motivated by this observation, we propose an appraoch that
incorporates prior information about the shapes of the
anatomical structures, as well as about the interaction of
neighboring shapes. We construct a statistical framework for
the segmentation problem, which captures information about
the shapes of coupled anatomical structures through prior
probability density functions. We nonparametrically estimate
these probability density functions from training shapes. We
develop an active contour-based segmentation algorithm that
combines image-based data with shape information. We
demonstrate the benefits of our approach over existing
methods through challenging segmentation scenarios on real
magnetic resonance images
Splenic infarction as a complication of celiac artery thromboembolism: an unusual cause of abdominal pain
Splenic infarction is a relatively uncommon diagnosis and this clinical presentation can mimic other causes of acute abdominal pain. Cardiologic and hematologic disorders are common reasons for this entity. There have been a few series and single case reports of splenic infarction published in peer-reviewed medical journals. We report a 53-year-old patient who had splenic infarction caused by celiac artery thromboembolism. The importance of this case, without any etiological predisposing factors, is that this kind of clinical situation should be considered in the differential diagnosis of abdominal pain
Search for dark matter produced in association with bottom or top quarks in âs = 13 TeV pp collisions with the ATLAS detector
A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fbâ1 of protonâproton collision data recorded by the ATLAS experiment at âs = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements
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