9,257 research outputs found
Adversarial Convolutional Networks with Weak Domain-Transfer for Multi-sequence Cardiac MR Images Segmentation
Analysis and modeling of the ventricles and myocardium are important in the
diagnostic and treatment of heart diseases. Manual delineation of those tissues
in cardiac MR (CMR) scans is laborious and time-consuming. The ambiguity of the
boundaries makes the segmentation task rather challenging. Furthermore, the
annotations on some modalities such as Late Gadolinium Enhancement (LGE) MRI,
are often not available. We propose an end-to-end segmentation framework based
on convolutional neural network (CNN) and adversarial learning. A dilated
residual U-shape network is used as a segmentor to generate the prediction
mask; meanwhile, a CNN is utilized as a discriminator model to judge the
segmentation quality. To leverage the available annotations across modalities
per patient, a new loss function named weak domain-transfer loss is introduced
to the pipeline. The proposed model is evaluated on the public dataset released
by the challenge organizer in MICCAI 2019, which consists of 45 sets of
multi-sequence CMR images. We demonstrate that the proposed adversarial
pipeline outperforms baseline deep-learning methods.Comment: 9 pages, 4 figures, conferenc
Projective non-Abelian Statistics of Dislocation Defects in a Z_N Rotor Model
Non-Abelian statistics is a phenomenon of topologically protected non-Abelian
Berry phases as we exchange quasiparticle excitations. In this paper, we
construct a Z_N rotor model that realizes a self-dual Z_N Abelian gauge theory.
We find that lattice dislocation defects in the model produce topologically
protected degeneracy. Even though dislocations are not quasiparticle
excitations, they resemble non-Abelian anyons with quantum dimension sqrt(N).
Exchanging dislocations can produces topologically protected projective
non-Abelian Berry phases. The dislocations, as projective non-Abelian anyons
can be viewed as a generalization of the Majorana zero modes.Comment: 4 pages + refs, 4 figures. RevTeX
Nonlinear Near-Field Microwave Microscope For RF Defect Localization in Superconductors
Niobium-based Superconducting Radio Frequency (SRF) cavity performance is
sensitive to localized defects that give rise to quenches at high accelerating
gradients. In order to identify these material defects on bulk Nb surfaces at
their operating frequency and temperature, it is important to develop a new
kind of wide bandwidth microwave microscopy with localized and strong RF
magnetic fields. By taking advantage of write head technology widely used in
the magnetic recording industry, one can obtain ~200 mT RF magnetic fields,
which is on the order of the thermodynamic critical field of Nb, on submicron
length scales on the surface of the superconductor. We have successfully
induced the nonlinear Meissner effect via this magnetic write head probe on a
variety of superconductors. This design should have a high spatial resolution
and is a promising candidate to find localized defects on bulk Nb surfaces and
thin film coatings of interest for accelerator applications.Comment: 4 pages, 6 figures Journal-ref: 2010 Applied Superconductivity
Conferenc
Spin current through an ESR quantum dot: A real-time study
The spin transport in a strongly interacting spin-pump nano-device is studied
using the time-dependent variational-matrix-product-state (VMPS) approach. The
precession magnetic field generates a dissipationless spin current through the
quantum dot. We compute the real time spin current away from the equilibrium
condition. Both transient and stationary states are reached in the simulation.
The essentially exact results are compared with those from the Hartree-Fock
approximation (HFA). It is found that correlation effect on the physical
quantities at quasi-steady state are captured well by the HFA for small
interaction strength. However the HFA misses many features in the real time
dynamics. Results reported here may shed light on the understanding of the
ultra-fast processes as well as the interplay of the non-equilibrium and
strongly correlated effect in the transport properties.Comment: 5 pages, 5 figure
Mgb2 Nonlinear Properties Investigated under Localized High RF Magnetic Field Excitation
In order to increase the accelerating gradient of Superconducting Radio
Frequency (SRF) cavities, Magnesium Diboride (MgB2) opens up hope because of
its high transition temperature and potential for low surface resistance in the
high RF field regime. However, due to the presence of the small superconducting
gap in the {\pi} band, the nonlinear response of MgB2 is potentially quite
large compared to a single gap s-wave superconductor (SC) such as Nb.
Understanding the mechanisms of nonlinearity coming from the two-band structure
of MgB2, as well as extrinsic sources, is an urgent requirement. A localized
and strong RF magnetic field, created by a magnetic write head, is integrated
into our nonlinear-Meissner-effect scanning microwave microscope [1]. MgB2
films with thickness 50 nm, fabricated by a hybrid physical-chemical vapor
deposition technique on dielectric substrates, are measured at a fixed location
and show a strongly temperature-dependent third harmonic response. We propose
that at least two mechanisms are responsible for this nonlinear response, one
of which involves vortex nucleation and penetration into the film. [1] T. M.
Tai, X. X. Xi, C. G. Zhuang, D. I. Mircea, S. M. Anlage, "Nonlinear Near-Field
Microwave Microscope for RF Defect Localization in Superconductors", IEEE
Trans. Appl. Supercond. 21, 2615 (2011).Comment: 6 pages, 6 figure
Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention and Dynamic Resampling
Automatic segmentation of multi-sequence (multi-modal) cardiac MR (CMR)
images plays a significant role in diagnosis and management for a variety of
cardiac diseases. However, the performance of relevant algorithms is
significantly affected by the proper fusion of the multi-modal information.
Furthermore, particular diseases, such as myocardial infarction, display
irregular shapes on images and occupy small regions at random locations. These
facts make pathology segmentation of multi-modal CMR images a challenging task.
In this paper, we present the Max-Fusion U-Net that achieves improved pathology
segmentation performance given aligned multi-modal images of LGE, T2-weighted,
and bSSFP modalities. Specifically, modality-specific features are extracted by
dedicated encoders. Then they are fused with the pixel-wise maximum operator.
Together with the corresponding encoding features, these representations are
propagated to decoding layers with U-Net skip-connections. Furthermore, a
spatial-attention module is applied in the last decoding layer to encourage the
network to focus on those small semantically meaningful pathological regions
that trigger relatively high responses by the network neurons. We also use a
simple image patch extraction strategy to dynamically resample training
examples with varying spacial and batch sizes. With limited GPU memory, this
strategy reduces the imbalance of classes and forces the model to focus on
regions around the interested pathology. It further improves segmentation
accuracy and reduces the mis-classification of pathology. We evaluate our
methods using the Myocardial pathology segmentation (MyoPS) combining the
multi-sequence CMR dataset which involves three modalities. Extensive
experiments demonstrate the effectiveness of the proposed model which
outperforms the related baselines.Comment: 13 pages, 7 figures, conference pape
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