292 research outputs found
Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-ray Image Segmentation
Automatic parsing of anatomical objects in X-ray images is critical to many
clinical applications in particular towards image-guided invention and workflow
automation. Existing deep network models require a large amount of labeled
data. However, obtaining accurate pixel-wise labeling in X-ray images relies
heavily on skilled clinicians due to the large overlaps of anatomy and the
complex texture patterns. On the other hand, organs in 3D CT scans preserve
clearer structures as well as sharper boundaries and thus can be easily
delineated. In this paper, we propose a novel model framework for learning
automatic X-ray image parsing from labeled CT scans. Specifically, a Dense
Image-to-Image network (DI2I) for multi-organ segmentation is first trained on
X-ray like Digitally Reconstructed Radiographs (DRRs) rendered from 3D CT
volumes. Then we introduce a Task Driven Generative Adversarial Network
(TD-GAN) architecture to achieve simultaneous style transfer and parsing for
unseen real X-ray images. TD-GAN consists of a modified cycle-GAN substructure
for pixel-to-pixel translation between DRRs and X-ray images and an added
module leveraging the pre-trained DI2I to enforce segmentation consistency. The
TD-GAN framework is general and can be easily adapted to other learning tasks.
In the numerical experiments, we validate the proposed model on 815 DRRs and
153 topograms. While the vanilla DI2I without any adaptation fails completely
on segmenting the topograms, the proposed model does not require any topogram
labels and is able to provide a promising average dice of 85% which achieves
the same level accuracy of supervised training (88%)
Studying quantum entanglement and quantum discord in the cavity QED models
Based on the two-qubit Jaynes-Cummings model - a common cavity quantum
electrodynamics model, and extending to modification of the three-qubit
Tavis-Cummings model, we investigate the quantum correlation between light and
matter in bipartite quantum systems. By resolving the quantum master equation,
we are able to derive the dissipative dynamics in open systems. To gauge the
degree of quantum entanglement in the two-qubit system, von Neumann entropy and
concurrence are introduced. Quantum discord, which can properly measure the
quantum correlation in both closed and open systems, is also introduced. In
addition, consideration is given to the impacts of initial entanglement and
dissipation strength on quantum discord. Finally we discussed two different
cases of nuclei motion: quantum and classical.Comment: 12 pages, 9 figure
Developing a novel rabbit model of atherosclerotic plaque rupture and thrombosis by cold-induced endothelial injury
<p>Abstract</p> <p>Background</p> <p>It is widely believed that atherosclerotic plaque rupture and subsequent thrombosis leads to acute coronary events and stroke. However, study of the mechanism and treatment of human plaque rupture is hampered by lack of a suitable animal model. Our aim was to develop a novel animal model of atherosclerotic plaque rupture to facilitate the study of human plaque disruption and thrombosis.</p> <p>Methods</p> <p>28 healthy male New Zealand white rabbits were randomly divided into two groups: rabbits in group A (n = 12) were only fed a high-fat diet for eight weeks; rabbits in group B (n = 16) underwent cold-induced endothelial injury with liquid nitrogen, then were given a high-fat diet for eight weeks. After completion of the preparatory regimen, triggering of plaque rupture was attempted by local injection of liquid nitrogen in both groups.</p> <p>Results</p> <p>All rabbits in group B had disrupted plaques or rupture-driven occlusive thrombus formation, but none in group A showed any effects. More importantly, the cold-induced plaques in our model were reminiscent of human atherosclerotic plaques in terms of architecture, cellular composition, growth characteristics, and patterns of lipid accumulation.</p> <p>Conclusion</p> <p>We successfully developed a novel rabbit model of atherosclerotic plaque rupture and thrombosis, which is simple, fast, inexpensive, and reproducible, and has a low mortality and a high yield of triggering. This model will allow us to better understand the mechanism of human plaque rupture and also to develop plaque-stabilizing therapies.</p
On the noise effect of test mass surface roughness in spaceborne gravitational wave detectors
Spaceborne gravitational wave detection mission has a demanding requirement
for the precision of displacement sensing, which is conducted by the
interaction between the laser field and test mass. However, due to the
roughness of the reflecting surface of the test mass, the displacement
measurement along the sensitive axis suffers a coupling error caused by the
residue motion of other degrees of freedom. In this article, we model the
coupling of the test mass residue random motion to the displacement sensing
along the sensitive axis and derived an analytical formula of the required
precision of the surface error for the spaceborne gravitational wave detectors.
Our result shows that this coupling error will not contaminate the picometer
displacement sensing for the test masses in the LISA pathfinder.Comment: 8 page
- …