279 research outputs found
Osteoporotic and Neoplastic Compression Fracture Classification on Longitudinal CT
Classification of vertebral compression fractures (VCF) having osteoporotic
or neoplastic origin is fundamental to the planning of treatment. We developed
a fracture classification system by acquiring quantitative morphologic and bone
density determinants of fracture progression through the use of automated
measurements from longitudinal studies. A total of 250 CT studies were acquired
for the task, each having previously identified VCFs with osteoporosis or
neoplasm. Thirty-six features or each identified VCF were computed and
classified using a committee of support vector machines. Ten-fold cross
validation on 695 identified fractured vertebrae showed classification
accuracies of 0.812, 0.665, and 0.820 for the measured, longitudinal, and
combined feature sets respectively.Comment: Contributed 4-Page Paper to be presented at the 2016 IEEE
International Symposium on Biomedical Imaging (ISBI), April 13-16, 2016,
Prague, Czech Republi
Segmental and transmural motion of the rat myocardium estimated using quantitative ultrasound with new strategies for infarct detection
Introduction: The estimation of myocardial motion abnormalities has great potential for the early diagnosis of myocardial infarction (MI). This study aims to quantitatively analyze the segmental and transmural myocardial motion in MI rats by incorporating two novel strategies of algorithm parameter optimization and transmural motion index (TMI) calculation.Methods: Twenty-one rats were randomly divided into three groups (n = 7 per group): sham, MI, and ischemia–reperfusion (IR) groups. Ultrasound radio-frequency (RF) signals were acquired from each rat heart at 1 day and 28 days after animal model establishment; thus, a total of six datasets were represented as Sham1, Sham28, MI1, MI28, IR1, and IR28. The systolic cumulative displacement was calculated using our previously proposed vectorized normalized cross-correlation (VNCC) method. A semiautomatic regional and layer-specific myocardium segmentation framework was proposed for transmural and segmental myocardial motion estimation. Two novel strategies were proposed: the displacement-compensated cross-correlation coefficient (DCCCC) for algorithm parameter optimization and the transmural motion index (TMI) for quantitative estimation of the cross-wall transmural motion gradient.Results: The results showed that an overlap value of 80% used in VNCC guaranteed a more accurate displacement calculation. Compared to the Sham1 group, the systolic myocardial motion reductions were significantly detected (p < 0.05) in the middle anteroseptal (M-ANT-SEP), basal anteroseptal (B-ANT-SEP), apical lateral (A-LAT), middle inferolateral (M-INF-LAT), and basal inferolateral (B-INF-LAT) walls as well as a significant TMI drop (p < 0.05) in the M-ANT-SEP wall in the MI1 rats; significant motion reductions (p < 0.05) were also detected in the B-ANT-SEP and A-LAT walls in the IR1 group. The motion improvements (p < 0.05) were detected in the M-INF-LAT wall in the MI28 group and the apical septal (A-SEP) wall in the IR28 group compared to the MI1 and IR1 groups, respectively.Discussion: Our results show that the MI-induced reductions and reperfusion-induced recovery in systolic myocardial contractility could be successfully evaluated using our method, and most post-MI myocardial segments could recover systolic function to various extents in the remodeling phase. In conclusion, the ultrasound-based quantitative estimation framework for estimating segmental and transmural motion of the myocardium proposed in our study has great potential for non-invasive, novel, and early MI detection
Improved graph cut model with features of superpixels and neighborhood patches for myocardium segmentation from ultrasound image
Ultrasound (US) imaging has the technical advantages for the functional evaluation of myocardium compared with other imaging modalities. However, it is a challenge of extracting the myocardial tissues from the background due to low quality of US imaging. To better extract the myocardial tissues, this study proposes a semi-supervised segmentation method of fast Superpixels and Neighborhood Patches based Continuous Min-Cut (fSP-CMC). The US image is represented by a graph, which is constructed depending on the features of superpixels and neighborhood patches
Deep convolutional networks for automated detection of posterior-element fractures on spine CT
Injuries of the spine, and its posterior elements in particular, are a common
occurrence in trauma patients, with potentially devastating consequences.
Computer-aided detection (CADe) could assist in the detection and
classification of spine fractures. Furthermore, CAD could help assess the
stability and chronicity of fractures, as well as facilitate research into
optimization of treatment paradigms.
In this work, we apply deep convolutional networks (ConvNets) for the
automated detection of posterior element fractures of the spine. First, the
vertebra bodies of the spine with its posterior elements are segmented in spine
CT using multi-atlas label fusion. Then, edge maps of the posterior elements
are computed. These edge maps serve as candidate regions for predicting a set
of probabilities for fractures along the image edges using ConvNets in a 2.5D
fashion (three orthogonal patches in axial, coronal and sagittal planes). We
explore three different methods for training the ConvNet using 2.5D patches
along the edge maps of 'positive', i.e. fractured posterior-elements and
'negative', i.e. non-fractured elements.
An experienced radiologist retrospectively marked the location of 55
displaced posterior-element fractures in 18 trauma patients. We randomly split
the data into training and testing cases. In testing, we achieve an
area-under-the-curve of 0.857. This corresponds to 71% or 81% sensitivities at
5 or 10 false-positives per patient, respectively. Analysis of our set of
trauma patients demonstrates the feasibility of detecting posterior-element
fractures in spine CT images using computer vision techniques such as deep
convolutional networks.Comment: To be presented at SPIE Medical Imaging, 2016, San Dieg
Residual Denoising Diffusion Models
We propose residual denoising diffusion models (RDDM), a novel dual diffusion
process that decouples the traditional single denoising diffusion process into
residual diffusion and noise diffusion. This dual diffusion framework expands
the denoising-based diffusion models, initially uninterpretable for image
restoration, into a unified and interpretable model for both image generation
and restoration by introducing residuals. Specifically, our residual diffusion
represents directional diffusion from the target image to the degraded input
image and explicitly guides the reverse generation process for image
restoration, while noise diffusion represents random perturbations in the
diffusion process. The residual prioritizes certainty, while the noise
emphasizes diversity, enabling RDDM to effectively unify tasks with varying
certainty or diversity requirements, such as image generation and restoration.
We demonstrate that our sampling process is consistent with that of DDPM and
DDIM through coefficient transformation, and propose a partially
path-independent generation process to better understand the reverse process.
Notably, our RDDM enables a generic UNet, trained with only an loss
and a batch size of 1, to compete with state-of-the-art image restoration
methods. We provide code and pre-trained models to encourage further
exploration, application, and development of our innovative framework
(https://github.com/nachifur/RDDM)
Excited quantum Hall effect: enantiomorphic flat bands in a Yin-Yang Kagome lattice
Quantum Hall effect (QHE) is one of the most fruitful research topics in
condensed-matter physics. Ordinarily, the QHE manifests in a ground state with
time-reversal symmetry broken by magnetization to carry a quantized chiral edge
conductivity around a two-dimensional insulating bulk. We propose a theoretical
concept and model of non-equilibrium excited-state QHE (EQHE) without intrinsic
magnetization. It arises from circularly polarized photoexcitation between two
enantiomorphic flat bands of opposite chirality, each supporting originally a
helical topological insulating state hosted in a Yin-Yang Kagome lattice. The
chirality of its edge state can be reversed by the handedness of light, instead
of the direction of magnetization as in the conventional quantum (anomalous)
Hall effect, offering a simple switching mechanism for quantum devices.
Implications and realization of EQHE in real materials are discussed
Application of statins in management of glioma: Recent advances
Gliomas are common primary intra-cerebral tumors in adults, and seriously threaten the health and life of affected patients, especially highly-malignant gliomas, such as glioblastoma multiforme. The clinical prognosis of glioma patients is poor, even for those who have received comprehensive treatment including surgery and concurrent chemo- and/or radio-therapy. As a structural analog of β-hydroxy-β- methylglutaryl coenzyme A (HMG CoA) reductase, statins are a restrictive enzyme in the metabolism of cholesterol. Recent laboratory studies and clinical trials have demonstrated that statins can exert antitumor effect, improve clinical prognosis and significantly prolong the survival time of glioma patients. This article is aimed to highlight the mechanisms of the anti-glioma effect of statins and review recent advances in the management of the disease.Keywords: Glioma, Glioblastoma multiforme, Intra-cerebral tumors, Statins, Prognosis, Survival time, β-Hydroxy-β-methylglutaryl coenzyme A (HMG CoA) reductas
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