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1.
L. Umapathy G.G. Perez-Carrillo M.B. Keerthivasan J.A. Rosado-Toro M.I. Altbach B. Winegar C. Weinkauf A. Bilgin for the Alzheimers Disease Neuroimaging Initiative 《AJNR. American journal of neuroradiology》2021,42(4):639
BACKGROUND AND PURPOSE:Accurate and reliable detection of white matter hyperintensities and their volume quantification can provide valuable clinical information to assess neurologic disease progression. In this work, a stacked generalization ensemble of orthogonal 3D convolutional neural networks, StackGen-Net, is explored for improving automated detection of white matter hyperintensities in 3D T2-FLAIR images.MATERIALS AND METHODS:Individual convolutional neural networks in StackGen-Net were trained on 2.5D patches from orthogonal reformatting of 3D-FLAIR (n = 21) to yield white matter hyperintensity posteriors. A meta convolutional neural network was trained to learn the functional mapping from orthogonal white matter hyperintensity posteriors to the final white matter hyperintensity prediction. The impact of training data and architecture choices on white matter hyperintensity segmentation performance was systematically evaluated on a test cohort (n = 9). The segmentation performance of StackGen-Net was compared with state-of-the-art convolutional neural network techniques on an independent test cohort from the Alzheimer’s Disease Neuroimaging Initiative-3 (n = 20).RESULTS:StackGen-Net outperformed individual convolutional neural networks in the ensemble and their combination using averaging or majority voting. In a comparison with state-of-the-art white matter hyperintensity segmentation techniques, StackGen-Net achieved a significantly higher Dice score (0.76 [SD, 0.08], F1-lesion (0.74 [SD, 0.13]), and area under precision-recall curve (0.84 [SD, 0.09]), and the lowest absolute volume difference (13.3% [SD, 9.1%]). StackGen-Net performance in Dice scores (median = 0.74) did not significantly differ (P = .22) from interobserver (median = 0.73) variability between 2 experienced neuroradiologists. We found no significant difference (P = .15) in white matter hyperintensity lesion volumes from StackGen-Net predictions and ground truth annotations.CONCLUSIONS:A stacked generalization of convolutional neural networks, utilizing multiplanar lesion information using 2.5D spatial context, greatly improved the segmentation performance of StackGen-Net compared with traditional ensemble techniques and some state-of-the-art deep learning models for 3D-FLAIR.White matter hyperintensities (WMHs) correspond to pathologic features of axonal degeneration, demyelination, and gliosis observed within cerebral white matter.1 Clinically, the extent of WMHs in the brain has been associated with cognitive impairment, Alzheimer’s disease and vascular dementia, and increased risk of stroke.2,3 The detection and quantification of WMH volumes to monitor lesion burden evolution and its correlation with clinical outcomes have been of interest in clinical research.4,5 Although the extent of WMHs can be visually scored,6 the categoric nature of such scoring systems makes quantitative evaluation of disease progression difficult. Manually segmenting WMHs is tedious, prone to inter- and intraobserver variability, and is, in most cases, impractical. Thus, there is an increased interest in developing fast, accurate, and reliable computer-aided automated techniques for WMH segmentation.Convolutional neural network (CNN)-based approaches have been successful in several semantic segmentation tasks in medical imaging.7 Recent works have proposed using deep learning–based methods for segmenting WMHs using 2D-FLAIR images.8-11 More recently, a WMH segmentation challenge12 was also organized (http://wmh.isi.uu.nl/) to facilitate comparison of automated segmentation of WMHs of presumed vascular origin in 2D multislice T2-FLAIR images. Architectures that used an ensemble of separately trained CNNs showed promising results in this challenge, with 3 of the top 5 winners using ensemble-based techniques.12Conventional 2D-FLAIR images are typically acquired with thick slices (3–4 mm) and possible slice gaps. Partial volume effects from a thick slice are likely to affect the detection of smaller lesions, both in-plane and out-of-plane. 3D-FLAIR images, with isotropic resolution, have been shown to achieve higher resolution and contrast-to-noise ratio13 and have shown promising results in MS lesion detection using 3D CNNs.14 Additionally, the isotropic resolution enables viewing and evaluation of the images in multiple planes. This multiplanar reformatting of 3D-FLAIR without the use of interpolating kernels is only possible due to the isotropic nature of the acquisition. Network architectures that use information from the 3 orthogonal views have been explored in recent works for CNN-based segmentation of 3D MR imaging data.15 The use of data from multiple planes allows more spatial context during training without the computational burden associated with full 3D training.16 The use of 3 orthogonal views simultaneously mirrors how humans approach this segmentation task.Ensembles of CNNs have been shown to average away the variances in the solution and the choice of model- and configuration-specific behaviors of CNNs.17 Traditionally, the solutions from these separately trained CNNs are combined by averaging or using a majority consensus. In this work, we propose the use of a stacked generalization framework (StackGen-Net) for combining multiplanar lesion information from 3D CNN ensembles to improve the detection of WMH lesions in 3D-FLAIR. A stacked generalization18 framework learns to combine solutions from individual CNNs in the ensemble. We systematically evaluated the performance of this framework and compared it with traditional ensemble techniques, such as averaging or majority voting, and state-of-the-art deep learning techniques. 相似文献
2.
3.
L. Misery E. Weisshaar E. Brenaut A.W.M. Evers F. Huet S. Ständer A. Reich E. Berardesca E. Serra-Baldrich J. Wallengren D. Linder J.W. Fluhr J.C. Szepietowski H. Maibach for the Special Interest Group on sensitive skin of the International Forum for the Study of Itch 《Journal of the European Academy of Dermatology and Venereology》2020,34(2):222-229
The special interest group on sensitive skin of the International Forum for the Study of Itch previously defined sensitive skin as a syndrome defined by the occurrence of unpleasant sensations (stinging, burning, pain, pruritus and tingling sensations) in response to stimuli that normally should not provoke such sensations. This additional paper focuses on the pathophysiology and the management of sensitive skin. Sensitive skin is not an immunological disorder but is related to alterations of the skin nervous system. Skin barrier abnormalities are frequently associated, but there is no cause and direct relationship. Further studies are needed to better understand the pathophysiology of sensitive skin – as well as the inducing factors. Avoidance of possible triggering factors and the use of well-tolerated cosmetics, especially those containing inhibitors of unpleasant sensations, might be suggested for patients with sensitive skin. The role of psychosocial factors, such as stress or negative expectations, might be relevant for subgroups of patients. To date, there is no clinical trial supporting the use of topical or systemic drugs in sensitive skin. The published data are not sufficient to reach a consensus on sensitive skin management. In general, patients with sensitive skin require a personalized approach, taking into account various biomedical, neural and psychosocial factors affecting sensitive skin. 相似文献
4.
Parallel enzyme‐linked immunosorbent assay screening for human immunodeficiency virus among blood donors in five Chinese blood centres: a retrospective analysis
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5.
Emmi Helle Aldo Córdova-Palomera Tiina Ojala Priyanka Saha Praneetha Potiny Stefan Gustafsson Erik Ingelsson Michael Bamshad Deborah Nickerson Jessica X. Chong University of Washington Center for Mendelian Genomics Euan Ashley James R. Priest 《Genetic epidemiology》2019,43(2):215-226
Loss of function variants in NOTCH1 cause left ventricular outflow tract obstructive defects (LVOTO). However, the risk conferred by rare and noncoding variants in NOTCH1 for LVOTO remains largely uncharacterized. In a cohort of 49 families affected by hypoplastic left heart syndrome, a severe form of LVOTO, we discovered predicted loss of function NOTCH1 variants in 6% of individuals. Rare or low-frequency missense variants were found in 16% of families. To make a quantitative estimate of the genetic risk posed by variants in NOTCH1 for LVOTO, we studied associations of 400 coding and noncoding variants in NOTCH1 in 1,085 cases and 332,788 controls from the UK Biobank. Two rare intronic variants in strong linkage disequilibrium displayed significant association with risk for LVOTO amongst European-ancestry individuals. This result was replicated in an independent analysis of 210 cases and 68,762 controls of non-European and mixed ancestry. In conclusion, carrying rare predicted loss of function variants in NOTCH1 confer significant risk for LVOTO. In addition, the two intronic variants seem to be associated with an increased risk for these defects. Our approach demonstrates the utility of population-based data sets in quantifying the specific risk of individual variants for disease-related phenotypes. 相似文献
6.
Comparative safety study on severe anemia by simeprevir versus telaprevir‐based triple therapy for chronic hepatitis C
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Eiichi Ogawa Norihiro Furusyo Eiji Kajiwara Hideyuki Nomura Akira Kawano Kazuhiro Takahashi Kazufumi Dohmen Takeaki Satoh Koichi Azuma Makoto Nakamuta Toshimasa Koyanagi Kazuhiro Kotoh Shinji Shimoda Jun Hayashi The Kyushu University Liver Disease Study Group 《Journal of gastroenterology and hepatology》2015,30(8):1309-1316
7.
Which infants with eczema are at risk of food allergy? Results from a population‐based cohort
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8.
Bi-Hua BIE Yong CHEN Zhi-Qi ZHAO Institute of Neurobiology Fu-Dan University Shanghai China Institute of Shanghai Physiology Chinese Academy of Sciences Shanghai China 《中国神经科学杂志》2006,(2)
Objective Ligustrazine, also named as tetramethylpyrazine, is a compound purified from Ligusticum chuanxiong hort and has ever been testified to be a calcium antagonist. The present investigation was to determine the antinoci-ceptive effect of ligustrazine and, if any, the peripheral ionic mechanism involved. Methods Paw withdrawal Latency ( PWL) to noxious heating was measured in vivo and whole-cell patch recording was performed on small dorsal root ganglion (DRG) neurons. Results Intraplantar injection of ligustrazine (0.5 mg in 25μl) significantly prolonged the withdrawal latency of ipsilateral hindpaw to noxious heating in the rat. Ligustrazine not only reversibly inhibited high-voltage gated calcium current of dorsal root ganglion (DRG) neuron in dose-dependent manner with IC50 of 1.89 mmol/L, but also decreased tetrodotoxin (TTX) -resistant sodium current in relatively selective and dose-dependent manner with IC50 of 2.49 mmol/L. Conclusion The results suggested that ligustrazine could elevate the threshold of thermal nociception through inhibiting the high-voltage gated calcium current and TTX-resistant sodium current of DRG neuron in the rat. 相似文献
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10.