4,678 research outputs found
Affine Registration of label maps in Label Space
Two key aspects of coupled multi-object shape\ud
analysis and atlas generation are the choice of representation\ud
and subsequent registration methods used to align the sample\ud
set. For example, a typical brain image can be labeled into\ud
three structures: grey matter, white matter and cerebrospinal\ud
fluid. Many manipulations such as interpolation, transformation,\ud
smoothing, or registration need to be performed on these images\ud
before they can be used in further analysis. Current techniques\ud
for such analysis tend to trade off performance between the two\ud
tasks, performing well for one task but developing problems when\ud
used for the other.\ud
This article proposes to use a representation that is both\ud
flexible and well suited for both tasks. We propose to map object\ud
labels to vertices of a regular simplex, e.g. the unit interval for\ud
two labels, a triangle for three labels, a tetrahedron for four\ud
labels, etc. This representation, which is routinely used in fuzzy\ud
classification, is ideally suited for representing and registering\ud
multiple shapes. On closer examination, this representation\ud
reveals several desirable properties: algebraic operations may\ud
be done directly, label uncertainty is expressed as a weighted\ud
mixture of labels (probabilistic interpretation), interpolation is\ud
unbiased toward any label or the background, and registration\ud
may be performed directly.\ud
We demonstrate these properties by using label space in a gradient\ud
descent based registration scheme to obtain a probabilistic\ud
atlas. While straightforward, this iterative method is very slow,\ud
could get stuck in local minima, and depends heavily on the initial\ud
conditions. To address these issues, two fast methods are proposed\ud
which serve as coarse registration schemes following which the\ud
iterative descent method can be used to refine the results. Further,\ud
we derive an analytical formulation for direct computation of the\ud
"group mean" from the parameters of pairwise registration of all\ud
the images in the sample set. We show results on richly labeled\ud
2D and 3D data sets
Efficient Image Evidence Analysis of CNN Classification Results
Convolutional neural networks (CNNs) define the current state-of-the-art for
image recognition. With their emerging popularity, especially for critical
applications like medical image analysis or self-driving cars, confirmability
is becoming an issue. The black-box nature of trained predictors make it
difficult to trace failure cases or to understand the internal reasoning
processes leading to results. In this paper we introduce a novel efficient
method to visualise evidence that lead to decisions in CNNs. In contrast to
network fixation or saliency map methods, our method is able to illustrate the
evidence for or against a classifier's decision in input pixel space
approximately 10 times faster than previous methods. We also show that our
approach is less prone to noise and can focus on the most relevant input
regions, thus making it more accurate and interpretable. Moreover, by making
simplifications we link our method with other visualisation methods, providing
a general explanation for gradient-based visualisation techniques. We believe
that our work makes network introspection more feasible for debugging and
understanding deep convolutional networks. This will increase trust between
humans and deep learning models.Comment: 14 pages, 19 figure
A Deep Learning based Fast Signed Distance Map Generation
Signed distance map (SDM) is a common representation of surfaces in medical
image analysis and machine learning. The computational complexity of SDM for 3D
parametric shapes is often a bottleneck in many applications, thus limiting
their interest. In this paper, we propose a learning based SDM generation
neural network which is demonstrated on a tridimensional cochlea shape model
parameterized by 4 shape parameters. The proposed SDM Neural Network generates
a cochlea signed distance map depending on four input parameters and we show
that the deep learning approach leads to a 60 fold improvement in the time of
computation compared to more classical SDM generation methods. Therefore, the
proposed approach achieves a good trade-off between accuracy and efficiency
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Label Space: A Coupled Multi-shape Representation
Richly labeled images representing several sub-structures of an organ occur quite frequently in medical images. For example, a typical brain image can be labeled into grey matter, white matter or cerebrospinal fluid, each of which may be subdivided further. Many manipulations such as interpolation, transformation, smoothing, or registration need to be performed on these images before they can be used in further analysis. In this work, we present a novel multi-shape representation and compare it with the existing representations to demonstrate certain advantages of using the proposed scheme. Specifically, we propose label space, a representation that is both flexible and well suited for coupled multi-shape analysis. Under this framework, object labels are mapped to vertices of a regular simplex, e.g the unit interval for two labels, a triangle for three labels, a tetrahedron for four labels, etc. This forms the basis of a convex linear structure with the property that all labels are equally spaced. We will demonstrate that this representation has several desirable properties: algebraic operations may be performed directly, label uncertainty is expressed equivalently as a weighted mixture of labels or in a probabilistic manner, and interpolation is unbiased toward any label or the background. In order to demonstrate these properties, we compare label space to signed distance maps as well as other implicit representations in tasks such as smoothing, interpolation, registration, and principal component analysis.Psycholog
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
Stellar magnetic field parameters from a Bayesian analysis of high-resolution spectropolarimetric observations
In this paper we describe a Bayesian statistical method designed to infer the
magnetic properties of stars observed using high-resolution circular
spectropolarimetry in the context of large surveys. This approach is well
suited for analysing stars for which the stellar rotation period is not known,
and therefore the rotational phases of the observations are ambiguous. The
model assumes that the magnetic observations correspond to a dipole oblique
rotator, a situation commonly encountered in intermediate and high-mass stars.
Using reasonable assumptions regarding the model parameter prior probability
density distributions, the Bayesian algorithm determines the posterior
probability densities corresponding to the surface magnetic field geometry and
strength by performing a comparison between the observed and computed Stokes V
profiles.
Based on the results of numerical simulations, we conclude that this method
yields a useful estimate of the surface dipole field strength based on a small
number (i.e. 1 or 2) of observations. On the other hand, the method provides
only weak constraints on the dipole geometry. The odds ratio, a parameter
computed by the algorithm that quantifies the relative appropriateness of the
magnetic dipole model versus the non-magnetic model, provides a more sensitive
diagnostic of the presence of weak magnetic signals embedded in noise than
traditional techniques.
To illustrate the application of the technique to real data, we analyse seven
ESPaDOnS and Narval observations of the early B-type magnetic star LP Ori.
Insufficient information is available to determine the rotational period of the
star and therefore the phase of the data; hence traditional modelling
techniques fail to infer the dipole strength. In contrast, the Bayesian method
allows a robust determination of the dipole polar strength,
G.Comment: Accepted for publication in Monthly Notices of the Royal Astronomical
Societ
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