240 research outputs found
Extraction of Airways with Probabilistic State-space Models and Bayesian Smoothing
Segmenting tree structures is common in several image processing
applications. In medical image analysis, reliable segmentations of airways,
vessels, neurons and other tree structures can enable important clinical
applications. We present a framework for tracking tree structures comprising of
elongated branches using probabilistic state-space models and Bayesian
smoothing. Unlike most existing methods that proceed with sequential tracking
of branches, we present an exploratory method, that is less sensitive to local
anomalies in the data due to acquisition noise and/or interfering structures.
The evolution of individual branches is modelled using a process model and the
observed data is incorporated into the update step of the Bayesian smoother
using a measurement model that is based on a multi-scale blob detector.
Bayesian smoothing is performed using the RTS (Rauch-Tung-Striebel) smoother,
which provides Gaussian density estimates of branch states at each tracking
step. We select likely branch seed points automatically based on the response
of the blob detection and track from all such seed points using the RTS
smoother. We use covariance of the marginal posterior density estimated for
each branch to discriminate false positive and true positive branches. The
method is evaluated on 3D chest CT scans to track airways. We show that the
presented method results in additional branches compared to a baseline method
based on region growing on probability images.Comment: 10 pages. Pre-print of the paper accepted at Workshop on Graphs in
Biomedical Image Analysis. MICCAI 2017. Quebec Cit
Compositional Design and Verification of a Multi-Agent System for One-to-Many Negotiation
A compositional verification method for multi-agent systems is presented and applied to a multi-agent system for one-to-many negotiation in the domain of load balancing of electricity use. Advantages of the method are that the complexity of the verification process is managed by compositionality, and that parts of the proofs can be reused in relation to reuse of components
CubeNet: Equivariance to 3D Rotation and Translation
3D Convolutional Neural Networks are sensitive to transformations applied to
their input. This is a problem because a voxelized version of a 3D object, and
its rotated clone, will look unrelated to each other after passing through to
the last layer of a network. Instead, an idealized model would preserve a
meaningful representation of the voxelized object, while explaining the
pose-difference between the two inputs. An equivariant representation vector
has two components: the invariant identity part, and a discernable encoding of
the transformation. Models that can't explain pose-differences risk "diluting"
the representation, in pursuit of optimizing a classification or regression
loss function.
We introduce a Group Convolutional Neural Network with linear equivariance to
translations and right angle rotations in three dimensions. We call this
network CubeNet, reflecting its cube-like symmetry. By construction, this
network helps preserve a 3D shape's global and local signature, as it is
transformed through successive layers. We apply this network to a variety of 3D
inference problems, achieving state-of-the-art on the ModelNet10 classification
challenge, and comparable performance on the ISBI 2012 Connectome Segmentation
Benchmark. To the best of our knowledge, this is the first 3D rotation
equivariant CNN for voxel representations.Comment: Preprin
Динамика интеграционных процессов ЕАЭС
Проведено исследование динамики интеграционных процессов и факторов, их определяющих. Отмечено, что ключевым моментом интеграции стран ЕАЭС является углубление кооперационных связей и формирование региональных цепочек добавленной стоимости
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