scholarly journals Multi-Sequence MRI Registration of Atherosclerotic Carotid Arteries Based on Cross-Scale Siamese Network

2021 ◽  
Vol 8 ◽  
Author(s):  
Xiaojie Huang ◽  
Lizhao Mao ◽  
Xiaoyan Wang ◽  
Zhongzhao Teng ◽  
Minghan Shao ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Loss Function ◽  
Carotid Atherosclerosis ◽  
Average Distance ◽  
High Sensitivity ◽  
Common Disease ◽  
Registration Accuracy ◽  
Data Set ◽  
Gaussian Smoothing ◽  
Registration Method

Cardiovascular disease (CVD) is a common disease with high mortality rate, and carotid atherosclerosis (CAS) is one of the leading causes of cardiovascular disease. Multisequence carotid MRI can not only identify carotid atherosclerotic plaque constituents with high sensitivity and specificity, but also obtain different morphological features, which can effectively help doctors improve the accuracy of diagnosis. However, it is difficult to evaluate the accurate evolution of local changes in carotid atherosclerosis in multi-sequence MRI due to the inconsistent parameters of different sequence images and the geometric space mismatch caused by the motion deviation of tissues and organs. To solve these problems, we propose a cross-scale multi-modal image registration method based on the Siamese U-Net. The network uses sub-networks with image inputs of different sizes to extract various features, and a special padding module is designed to make the network available for training on cross-scale features. In addition, to improve the registration performance, a multi-scale loss function under Gaussian smoothing is applied for optimization. For the experiments, we have collected a multi-sequence MRI image dataset from 11 patients with carotid atherosclerosis for a retrospective study. We evaluate our overall architectures by cross-validation on our carotid dataset. The experimental results show that our method can generate precise and reliable results with cross-scale multi-sequence inputs and the registration accuracy can be greatly improved by using the Gaussian smoothing loss function. The DSC of our Siamese structure can reach 84.1% on the carotid data set with cross-size input. With the use of GDSC loss, the average DSC can be improved by 5.23%, while the average distance between fixed landmarks and moving landmarks can be decreased by 6.46%.Our code is made publicly available at: https://github.com/MingHan98/Cross-scale-Siamese-Unet.

Spectroscopy-Based Mapping with Scanning Microwave Impedance Microscopy

2018 ◽  
Author(s):  
Peter De Wolf ◽  
Zhuangqun Huang ◽  
Bede Pittenger
Keyword(s):  
Single Point ◽  
Electrical Characterization ◽  
Principal Component ◽  
High Sensitivity ◽  
Data Cube ◽  
Nanometer Scale ◽  
Learning Approaches ◽  
Data Set ◽  
3D Data ◽  
Higher Dimensional

Abstract Methods are available to measure conductivity, charge, surface potential, carrier density, piezo-electric and other electrical properties with nanometer scale resolution. One of these methods, scanning microwave impedance microscopy (sMIM), has gained interest due to its capability to measure the full impedance (capacitance and resistive part) with high sensitivity and high spatial resolution. This paper introduces a novel data-cube approach that combines sMIM imaging and sMIM point spectroscopy, producing an integrated and complete 3D data set. This approach replaces the subjective approach of guessing locations of interest (for single point spectroscopy) with a big data approach resulting in higher dimensional data that can be sliced along any axis or plane and is conducive to principal component analysis or other machine learning approaches to data reduction. The data-cube approach is also applicable to other AFM-based electrical characterization modes.

Correlation of Waist to Calf Circumference Ratio and Carotid Intima Media Thickness in Diabetes Mellitus

2020 ◽  
Vol 16 ◽  
Author(s):  
Harish A Rao ◽  
Prakash Harischandra ◽  
Srikanth Yadav
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Cardiovascular Disease ◽  
Risk Factor ◽  
Carotid Atherosclerosis ◽  
Intima Media Thickness ◽  
Carotid Intima Media Thickness ◽  
Calf Circumference ◽  
Media Thickness

Introduction: Diabetes mellitus is a well-known risk factor for cardiovascular disease, because of the accelerated process of atherosclerosis. Obesity is an established risk factor and has gained immense importance in recent studies as an important risk factor for clinical cardiovascular disease, yet the fundamental component stays unclear. Calf circumference is another form for lean mass and peripheral subcutaneous fat and is inversely associated with occurrence of carotid plaques (CP). Multiplicative and opposite effects of both Calf Circumference (CC) and Waist Circumference (WC) in event of CP suggest that the two measures should be taken into account commonly while assessing vascular risk profile. Aim & Objective: To ascertain if waist to calf circumference ratio (WCR) is a marker of Carotid atherosclerosis in patients with type 2 diabetes mellitus. To asses s the correlation between waist to calf circumference ratio and carotid intima media thickness (CIMT ) in patients with Type 2 diabetes. Materials and methods: A cross sectional study at Hospital affiliated to Kasturba Medical college Mangalore from Sept 2016 to Sept 2018 . Method of study: Patients with type 2 DM as per ADA criteria, age >18years are recruited for the study. Results and discussion: In our study with 150 population 25 patients had carotid atherosclerosis and 20 patients had CIMT>1.1. The waist circumference in patients with CA is in the range of 93.07 and 99.85 & the CC in patients with CA is in the range of 29.49 to 31.25. The WCR in patients with CA is in the range of 3.12 to 3.26. The difference was statistically significant with a p value of <0.05. In our study it was found that WC and WCR correlated well with carotid atherosclerosis, and surprisingly calf circumference also correlated with carotid atherosclerosis but not as significant as both WC and WCR. Conclusion: To conclude, in our population based study of 150 subjects we found that carotid atherosclerosis is significantly more in people with increased waist calf circumference ratio. WCR may be a new, useful and practical anthropometric index that facilitates the early identification of diabetic subjects with high risk for cardiovascular disease. Validation of this finding in individual populations is required. Future studies should test the association of calf circumference with carotid intima media thickness and carotid plaques using better measures than ultrasound such as magnetic resonance imaging. Further research focusing on underlying mechanisms in the role of lean mass and peripheral fat mass is required.

scholarly journals Classification of jujube defects in small data sets based on transfer learning

2021 ◽  
Author(s):  
Jianping Ju ◽  
Hong Zheng ◽  
Xiaohang Xu ◽  
Zhongyuan Guo ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Loss Function ◽  
Training Model ◽  
Parameter Distribution ◽  
Test Accuracy ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

scholarly journals Fall incidents in nursing home residents: development of a predictive clinical rule (FINDER)

BMJ Open ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. e042941
Author(s):  
Vanja Milosevic ◽  
Aimee Linkens ◽  
Bjorn Winkens ◽  
Kim P G M Hurkens ◽  
Dennis Wong ◽  
...  
Keyword(s):  
Nursing Home ◽  
Fall Risk ◽  
Nursing Home Residents ◽  
High Sensitivity ◽  
Secondary Outcome ◽  
Operating Characteristics ◽  
Retrospective Case ◽  
Data Set ◽  
Fall Incident ◽  
Clinical Rule

ObjectivesTo develop (part I) and validate (part II) an electronic fall risk clinical rule (CR) to identify nursing home residents (NH-residents) at risk for a fall incident.DesignObservational, retrospective case–control study.SettingNursing homes.ParticipantsA total of 1668 (824 in part I, 844 in part II) NH-residents from the Netherlands were included. Data of participants from part I were excluded in part II.Primary and secondary outcome measuresDevelopment and validation of a fall risk CR in NH-residents. Logistic regression analysis was conducted to identify the fall risk-variables in part I. With these, three CRs were developed (ie, at the day of the fall incident and 3 days and 5 days prior to the fall incident). The overall prediction quality of the CRs were assessed using the area under the receiver operating characteristics (AUROC), and a cut-off value was determined for the predicted risk ensuring a sensitivity ≥0.85. Finally, one CR was chosen and validated in part II using a new retrospective data set.ResultsEleven fall risk-variables were identified in part I. The AUROCs of the three CRs form part I were similar: the AUROC for models I, II and III were 0.714 (95% CI: 0.679 to 0.748), 0.715 (95% CI: 0.680 to 0.750) and 0.709 (95% CI: 0.674 to 0.744), respectively. Model III (ie, 5 days prior to the fall incident) was chosen for validation in part II. The validated AUROC of the CR, obtained in part II, was 0.603 (95% CI: 0.565 to 0.641) with a sensitivity of 83.41% (95% CI: 79.44% to 86.76%) and a specificity of 27.25% (95% CI 23.11% to 31.81%).ConclusionMedication data and resident characteristics alone are not sufficient enough to develop a successful CR with a high sensitivity and specificity to predict fall risk in NH-residents.Trial registration numberNot available.

scholarly journals The Study of Multiple Classes Boosting Classification Method Based on Local Similarity

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 37
Author(s):  
Shixun Wang ◽  
Qiang Chen
Keyword(s):  
Image Retrieval ◽  
Loss Function ◽  
Single Mode ◽  
Local Similarity ◽  
Text And Image ◽  
Data Set ◽  
Standard Data ◽  
Weak Learner ◽  
Great Progress ◽  
Data Points

Boosting of the ensemble learning model has made great progress, but most of the methods are Boosting the single mode. For this reason, based on the simple multiclass enhancement framework that uses local similarity as a weak learner, it is extended to multimodal multiclass enhancement Boosting. First, based on the local similarity as a weak learner, the loss function is used to find the basic loss, and the logarithmic data points are binarized. Then, we find the optimal local similarity and find the corresponding loss. Compared with the basic loss, the smaller one is the best so far. Second, the local similarity of the two points is calculated, and then the loss is calculated by the local similarity of the two points. Finally, the text and image are retrieved from each other, and the correct rate of text and image retrieval is obtained, respectively. The experimental results show that the multimodal multi-class enhancement framework with local similarity as the weak learner is evaluated on the standard data set and compared with other most advanced methods, showing the experience proficiency of this method.

scholarly journals Correlates of serum hepcidin levels and its association with cardiovascular disease in an elderly general population

2016 ◽  
Vol 54 (1) ◽  
Author(s):  
Raimund Pechlaner ◽  
Stefan Kiechl ◽  
Manuel Mayr ◽  
Peter Santer ◽  
Siegfried Weger ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Alcohol Intake ◽  
Carotid Atherosclerosis ◽  
Blood Parameters ◽  
Population Based ◽  
Dietary Intakes ◽  
Iron Stores ◽  
Serum Hepcidin ◽  
General Community ◽  
Incident Cardiovascular Disease

AbstractThe expression of the key iron regulatory hormone hepcidin is regulated by iron availability, inflammation, hormones, hypoxia, and anaemia. Increased serum concentrations of hepcidin have recently been linked to atherosclerosis. We studied demographic, haematologic, biochemical, and dietary correlates of serum hepcidin levels and its associations with incident cardiovascular disease and with carotid atherosclerosis.Serum hepcidin concentrations were measured by tandem mass spectrometry in samples taken in 2000 from 675 infection-free participants of the prospective population-based Bruneck study (age, mean±standard deviation, 66.0±10.2; 48.1% male). Blood parameters were measured by standard methods. Dietary intakes of iron and alcohol were surveyed with a food frequency questionnaire. Carotid atherosclerosis (365 cases) was assessed by ultrasound and subjects were observed for incident stroke, myocardial infarction, or sudden cardiac death (91 events) until 2010.Median (interquartile range) hepcidin levels were 2.27 nM (0.86, 4.15). Most hepcidin correlates were in line with hepcidin as an indicator of iron stores. Independently of ferritin, hepcidin was related directly to physical activity (p=0.024) and fibrinogen (p<0.0001), and inversely to alcohol intake (p=0.006), haemoglobin (p=0.027), and γ-glutamyltransferase (p<0.0001). Hepcidin and hepcidin-to-ferritin ratio were not associated with prevalent carotid atherosclerosis (p=0.43 and p=0.79) or with incident cardiovascular disease (p=0.62 and p=0.33).In this random sample of the general community, fibrinogen and γ-glutamyltransferase were the most significant hepcidin correlates independent of iron stores, and hepcidin was related to neither atherosclerosis nor cardiovascular disease.

scholarly journals AUTOMATIC MRF-BASED REGISTRATION OF HIGH RESOLUTION SATELLITE VIDEO DATA

2016 ◽  
Vol III-1 ◽  
pp. 121-128 ◽  
Author(s):  
C. Platias ◽  
M. Vakalopoulou ◽  
K. Karantzalos
Keyword(s):  
High Resolution ◽  
Markov Random Fields ◽  
Video Data ◽  
Registration Accuracy ◽  
Registration Method ◽  
Computational Performance ◽  
Markov Random ◽  
Video Frames ◽  
Reference Map ◽  
The Cost

In this paper we propose a deformable registration framework for high resolution satellite video data able to automatically and accurately co-register satellite video frames and/or register them to a reference map/image. The proposed approach performs non-rigid registration, formulates a Markov Random Fields (MRF) model, while efficient linear programming is employed for reaching the lowest potential of the cost function. The developed approach has been applied and validated on satellite video sequences from Skybox Imaging and compared with a rigid, descriptor-based registration method. Regarding the computational performance, both the MRF-based and the descriptor-based methods were quite efficient, with the first one converging in some minutes and the second in some seconds. Regarding the registration accuracy the proposed MRF-based method significantly outperformed the descriptor-based one in all the performing experiments.

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