Search results for "20(60)"

Deep learning approach for the segmentation of aneurysmal ascending aorta.

2020

Diagnosis of ascending thoracic aortic aneurysm (ATAA) is based on the measurement of the maximum aortic diameter, but size is not a good predictor of the risk of adverse events. There is growing interest in the development of novel image-derived risk strategies to improve patient risk management towards a highly individualized level. In this study, the feasibility and efficacy of deep learning for the automatic segmentation of ATAAs was investigated using UNet, ENet, and ERFNet techniques. Specifically, CT angiography done on 72 patients with ATAAs and different valve morphology (i.e., tricuspid aortic valve, TAV, and bicuspid aortic valve, BAV) were semi-automatically segmented with Mimic…

Constitutive modeling of ascending thoracic aortic aneurysms using microstructural parameters.

2016

Ascending thoracic aortic aneurysm (ATAA) has been associated with diminished biomechanical strength and disruption in the collagen fiber microarchitecture. Additionally, the congenital bicuspid aortic valve (BAV) leads to a distinct extracellular matrix structure that may be related to ATAA development at an earlier age than degenerative aneurysms arising in patients with the morphological normal tricuspid aortic valve (TAV). The purpose of this study was to model the fiber-reinforced mechanical response of ATAA specimens from patients with either BAV or TAV. This was achieved by combining image-analysis derived parameters of collagen fiber dispersion and alignment with tensile testing dat…

Statistical shape analysis of ascending thoracic aortic aneurysm: Correlation between shape and biomechanical descriptors

2020

An ascending thoracic aortic aneurysm (ATAA) is a heterogeneous disease showing different patterns of aortic dilatation and valve morphologies, each with distinct clinical course. This study aimed to explore the aortic morphology and the associations between shape and function in a population of ATAA, while further assessing novel risk models of aortic surgery not based on aortic size. Shape variability of n = 106 patients with ATAA and different valve morphologies (i.e., bicuspid versus tricuspid aortic valve) was estimated by statistical shape analysis (SSA) to compute a mean aortic shape and its deformation. Once the computational atlas was built, principal component analysis (PCA) allow…

Dielectric Behavior of Aqueous Solutions of α,β-Poly(aspartyl hydrazide) and α,β-Poly(N-hydroxyethyl aspartamide): An Investigation of the Structural…

1994

The dielectric properties of aqueous solutions of α,β-poly(aspartyl hydrazide) (PAHy) and of α,β-poly( N-hydroxyethyl aspartamide) (PHEA) were measured at 25 ° C in the frequency range of 100 MHz to 15 GHz using a time domain reflection method (TDR). Single time relaxation processes were found at 2 GHz and 15 GHz, respectively. The low frequency dispersion was inter preted in terms of the dynamics of polymeric segments based on the dielectric relaxation strength and the relaxation time. The high frequency process which is attributed to the rotational relaxation of water, indicated that water mole cules surrounding the polymeric backbone and in the pure state have a similar rotational behav…

FastEMD–CCA algorithm for unsupervised and fast removal of eyeblink artifacts from electroencephalogram

2020

Abstract Online detection and removal of eye blink (EB) artifacts from electroencephalogram (EEG) would be very useful in medical diagnosis and brain computer interface (BCI). In this work, approaches that combine unsupervised eyeblink artifact detection with empirical mode decomposition (EMD), and canonical correlation analysis (CCA), are proposed to automatically identify eyeblink artifacts and remove them in an online manner. First eyeblink artifact regions are automatically identified and an eyeblink artifact template is extracted via EMD, which incorporates an alternate interpolation technique, the Akima spline interpolation. The removal of eyeblink artifact components relies on the el…

Adaptive Continuous Feature Binarization for Tsetlin Machines Applied to Forecasting Dengue Incidences in the Philippines

2020

The Tsetlin Machine (TM) is a recent interpretable machine learning algorithm that requires relatively modest computational power, yet attains competitive accuracy in several benchmarks. TMs are inherently binary; however, many machine learning problems are continuous. While binarization of continuous data through brute-force thresholding has yielded promising accuracy, such an approach is computationally expensive and hinders extrapolation. In this paper, we address these limitations by standardizing features to support scale shifts in the transition from training data to real-world operation, typical for e.g. forecasting. For scalability, we employ sampling to reduce the number of binariz…

Intrusion Detection with Interpretable Rules Generated Using the Tsetlin Machine

2020

The rapid deployment in information and communication technologies and internet-based services have made anomaly based network intrusion detection ever so important for safeguarding systems from novel attack vectors. To this date, various machine learning mechanisms have been considered to build intrusion detection systems. However, achieving an acceptable level of classification accuracy while preserving the interpretability of the classification has always been a challenge. In this paper, we propose an efficient anomaly based intrusion detection mechanism based on the Tsetlin Machine (TM). We have evaluated the proposed mechanism over the Knowledge Discovery and Data Mining 1999 (KDD’99) …

Towards understanding the complexity of cardiovascular oscillations: Insights from information theory.

2018

Abstract Cardiovascular complexity is a feature of healthy physiological regulation, which stems from the simultaneous activity of several cardiovascular reflexes and other non-reflex physiological mechanisms. It is manifested in the rich dynamics characterizing the spontaneous heart rate and blood pressure variability (HRV and BPV). The present study faces the challenge of disclosing the origin of short-term HRV and BPV from the statistical perspective offered by information theory. To dissect the physiological mechanisms giving rise to cardiovascular complexity in different conditions, measures of predictive information, information storage, information transfer and information modificati…

The effect of silver nanoparticles, zinc oxide nanoparticles, and titanium dioxide nanoparticles on the push-out bond strength of fiber posts

2019

Background This study was undertaken to investigate the effect of intraradicular dentin pretreatment with silver nanoparticles (SNPs), zinc oxide nanoparticles (ZNPs), and titanium oxide nanoparticles (TNPs) on the push-out bond strength (PBS) of fiber posts to root dentin using two types of resin cements. Material and methods Eighty single-rooted human premolar roots were randomly divided into eight groups after endodontic treatment and post space preparation, according to the type of intraradicular dentin pretreatment with different nanoparticle solutions (n=20). The groups included no pretreatment (control) and pretreatments with SNPs, ZNPs, and TNPs. Each group was divided into 2 subgro…

2020

Large segmental bone defects occurring after trauma, bone tumors, infections or revision surgeries are a challenge for surgeons. The aim of our study was to develop a new biomaterial utilizing simple and cheap 3D-printing techniques. A porous polylactide (PLA) cylinder was printed and functionalized with stromal-derived factor 1 (SDF-1) or bone morphogenetic protein 7 (BMP-7) immobilized in collagen type I. Biomechanical testing proved biomechanical stability and the scaffolds were implanted into a 6 mm critical size defect in rat femur. Bone growth was observed via x-ray and after 8 weeks, bone regeneration was analyzed with µCT and histological staining methods. Development of non-unions …