65 research outputs found
A Deep Segmentation Network of Stent Structs Based on IoT for Interventional Cardiovascular Diagnosis
[EN] The Internet of Things (IoT) technology has been widely introduced to the existing medical system. An eHealth system based on IoT devices has gained widespread popularity. In this article, we propose an IoT eHealth framework to provide an autonomous solution for patients with interventional cardiovascular diseases. In this framework, wearable sensors are used to collect a patient's health data, which is daily monitored by a remote doctor. When the monitoring data is abnormal, the remote doctor will ask for image acquisition of the patient's cardiovascular internal conditions. We leverage edge computing to classify these training images by the local base classifier; thereafter, pseudo-labels are generated according to its output. Moreover, a deep segmentation network is leveraged for the segmentation of stent structs in intravascular optical coherence tomography and intravenous ultrasound images of patients. The experimental results demonstrate that remote and local doctors perform real-time visual communication to complete telesurgery. In the experiments, we adopt the U-net backbone with a pretrained SeResNet34 as the encoder to segment the stent structs. Meanwhile, a series of comparative experiments have been conducted to demonstrate the effectiveness of our method based on accuracy, sensitivity, Jaccard, and dice.This work was supported by the National Key Research and Development Program of China (Grant no. 2020YFB1313703), the National Natural Science Foundation of China (Grant no. 62002304), and the Natural Science Foundation of Fujian Province of China (Grant no. 2020J05002).Huang, C.; Zong, Y.; Chen, J.; Liu, W.; Lloret, J.; Mukherjee, M. (2021). A Deep Segmentation Network of Stent Structs Based on IoT for Interventional Cardiovascular Diagnosis. IEEE Wireless Communications. 28(3):36-43. https://doi.org/10.1109/MWC.001.2000407S364328
Lumen contour segmentation in ivoct based on n-type cnn
Automatic segmentation of lumen contour plays an important role in medical imaging and diagnosis, which is the first step towards the evaluation of morphology of vessels under analysis and the identification of possible atherosclerotic lesions. Meanwhile, quantitative information can only be obtained with segmentation, contributing to the appearance of novel methods which can be successfully applied to intravascular optical coherence tomography (IVOCT) images. This paper proposed a new end-to-end neural network (N-Net) for the automatic lumen segmentation, using multi-scale features based deep neural network, for IVOCT images. The architecture of the N-Net contains a multi-scale input layer, a N-type convolution network layer and a cross-entropy loss function. The multi-scale input layer in the proposed N-Net is designed to avoid the loss of information caused by pooling in traditional U-Net and also enriches the detailed information in each layer. The N-type convolutional network is proposed as the framework in the whole deep architecture. Finally, the loss function guarantees the degree of fidelity between the output of proposed method and the manually labeled output. In order to enlarge the training set, data augmentation is also introduced. We evaluated our method against loss, accuracy, recall, dice similarity coefficient, jaccard similarity coefficient and specificity. The experimental results presented in this paper demonstrate the superior performance of the proposed N-Net architecture, comparing to some existing networks, for enhancing the precision of automatic lumen segmentation and increasing the detailed information of edges of the vascular lumen
Disentangling the effects of vapor pressure deficit on northern terrestrial vegetation productivity
The impact of atmospheric vapor pressure deficit (VPD) on plant photosynthesis has long been acknowledged, but large interactions with air temperature (T) and soil moisture (SM) still hinder a complete understanding of the influence of VPD on vegetation production across various climate zones. Here, we found a diverging response of productivity to VPD in the Northern Hemisphere by excluding interactive effects of VPD with T and SM. The interactions between VPD and T/SM not only offset the potential positive impact of warming on vegetation productivity but also amplifies the negative effect of soil drying. Notably, for high-latitude ecosystems, there occurs a pronounced shift in vegetation productivity\u27s response to VPD during the growing season when VPD surpasses a threshold of 3.5 to 4.0 hectopascals. These results yield previously unknown insights into the role of VPD in terrestrial ecosystems and enhance our comprehension of the terrestrial carbon cycle\u27s response to global warming
An Early Diagnosis of Oral Cancer based on Three-Dimensional Convolutional Neural Networks
Three-dimensional convolutional neural networks (3DCNNs), a rapidly evolving modality of deep learning, has gained popularity in many fields. For oral cancers, CT images are traditionally processed using two-dimensional input, without considering information between lesion slices. In this paper, we established a 3DCNNs-based image processing algorithm for the early diagnosis of oral cancers, which was compared with a 2DCNNs-based algorithm. The 3D and 2D CNNs were constructed using the same hierarchical structure to profile oral tumors as benign or malignant. Our results showed that 3DCNNs with dynamic characteristics of the enhancement rate image performed better than 2DCNNS with single enhancement sequence for the discrimination of oral cancer lesions. Our data indicate that spatial features and spatial dynamics extracted from 3DCNNs may inform future design of CT-assisted diagnosis system
Facilitation of AMPA Receptor Synaptic Delivery as a Molecular Mechanism for Cognitive Enhancement
A small peptide from a neuronal cell adhesion molecule enhances synaptic plasticity in the hippocampus and results in improved cognitive performance in rats
Moving Window Differential Evolution Independent Component Analysis-Based Operational Modal Analysis for Slow Linear Time-Varying Structures
In order to identify time-varying transient modal parameters only from nonstationary vibration response measurement signals for slow linear time-varying (SLTV) structures which are weakly damped, a moving window differential evolution (DE) independent component analysis- (ICA-) based operational modal analysis (OMA) method is proposed in this paper. Firstly, in order to overcome the problems in traditional ICA-based OMA, such as easy to go into local optima and difficult-to-identify high-order modal parameters, we combine DE with ICA and propose a differential evolution independent component analysis- (DEICA-) based OMA method for linear time invariant (LTI) structures. Secondly, we combine the moving widow technique with DEICA and propose a moving window differential evolution independent component analysis- (MWDEICA-) based OMA method for SLTV structures. The MWDEICA-based OMA method has high global searching ability, robustness, and complexity of time and space. The modal identification results in a three-degree-of-freedom structure with slow time-varying mass show that this MWDEICA-based OMA method can identify transient time-varying modal parameters effectively only from nonstationary vibration response measurement signals and has better performances than moving window traditional ICA-based OMA
The Advancement in Spring Vegetation Phenology in the Northern Hemisphere Will Reverse After 2060 Under Future Moderate Warming Scenarios
Abstract Global warming has largely advanced spring vegetation phenology, which has subsequently affected terrestrial carbon and water cycles. However, further shifts in vegetation phenology under future climate change remain unclear. We estimated the start of the growing season (SOS) by applying multiple extraction methods based on the NDVI3g data set, and then parameterized and evaluated 11 spring vegetation phenology models that included chilling, forcing, and the photoperiod. Based on scenario data from three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585) derived from eight climate models, future vegetation phenology was predicted using the phenology models. Results showed that all the phenology models performed better than the NULL model (mean of the SOS), with the performance of one‐phase models broadly matching that of two‐phase models, although the best models varied by vegetation type. The spatial pattern of simulated SOS was similar among the models, and it explained >75% of the variation. Based on the mean predicted SOS, we found that spring vegetation phenology will continue to advance under strong warming conditions (SSP245 and SSP585), but that the trend of advance will reverse at around 2060 under the SSP126 scenario. The continued trend in SOS advance is likely related to rapid forcing fulfillment under stronger warming conditions. However, under moderate warming, chilling might be reduced and it might require longer to compensate for higher forcing, which ultimately would result in SOS delay. Our findings highlight that trends will likely change under different warming conditions, potentially causing widespread impact on species interaction, biodiversity, and ecosystem function
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