Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

General General

Iron oxide magnetic nanoparticles based low-field MR thermometry.

In Nanotechnology

This paper reports on a highly accurate approach of magnetic resonance (MR) thermometry using iron oxide magnetic nanoparticles (MNPs) as temperature sensors. An empirical model for the description of the temperature dependentR2relaxation rate is proposed by taking into account the temperature sensitivity of the MNP magnetization. The temperature sensitivity of the MNP magnetization (η) and the temperature sensitivity of theR2relaxation rate (κ) are simulated with the proposed empirical models to investigate their dependence on the magnetic field and the particle size. Simulation results show the existence of optimal magnetic fieldsH οη andH οκ that maximize the temperature sensitivitiesηandκ. Furthermore, simulations and experiments demonstrate that the optimal magnetic fieldH οη (H οκ ) decreases with increasing the particle size. Experiments on temperature dependentR2relaxation rate are performed at different magnetic fields for MNP samples with different iron concentration. Experimental results show that the proposed MR thermometry using MNPs as temperature sensors allows a temperature estimation accuracy of about 0.05 °C. We believe that the achieved approach of highly accurate MR thermometry is of great interest and significance to biomedicine and biology.

Zhang Yapeng, Guo Silin, Zhang Pu, Zhong Jing, Liu Wenzhong


<i>R</i><sub>2</sub> relaxation rate, MR thermometry, iron oxide magnetic nanoparticles, low-field NMR

General General

Stress detection using ECG and EMG signals: A comprehensive study.

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVE : In recent years, stress and mental health have been considered as important worldwide concerns. Stress detection using physiological signals such as electrocardiogram (ECG), skin conductance (SC), electromyogram (EMG) and electroencephalogram (EEG) is a traditional approach. However, the effect of stress on the EMG signal of different muscles and the efficacy of combination of the EMG and other biological signals for stress detection have not been taken into account yet. This paper presents a comprehensive review of the EMG signal of the right and left trapezius and right and left erector spinae muscles for multi-level stress recognition. Also, the ECG signal was employed to evaluate the efficacy of EMG signals for stress detection.

METHODS : Both EMG and ECG signals were acquired simultaneously from 34 healthy students (23 females and 11 males, aged 20-37 years). Mental arithmetic, Stroop color-word test, time pressure, and stressful environment were employed to induce stress in the laboratory.

RESULTS : The accuracies of stress recognition in two, three and four levels were 100%, 97.6%, and 96.2%, respectively, obtained from the distinct combination of feature selection and machine learning algorithms.

CONCLUSIONS : The comparison of stress detection accuracies resulted from EMG and ECG indicators demonstrated the strong ability and the effectiveness of EMG signal for multi-level stress detection.

Pourmohammadi Sara, Maleki Ali


Electrocardiogram, Electromyogram, Erector spinae muscle, Heart rate variability, Multi-level stress detection, Stress-inducing protocol, Trapezius muscle

Radiology Radiology

Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study.

In European journal of radiology ; h5-index 47.0

PURPOSE : To develop a deep learning-based method to assist radiologists to fast and accurately identify patients with COVID-19 by CT images.

METHODS : We retrospectively collected chest CT images of 495 patients from three hospitals in China. 495 datasets were randomly divided into 395 cases (80%, 294 of COVID-19, 101 of other pneumonia) of the training set, 50 cases (10%, 37 of COVID-19, 13 of other pneumonia) of the validation set and 50 cases (10%, 37 of COVID-19, 13 of other pneumonia) of the testing set. We trained a multi-view fusion model using deep learning network to screen patients with COVID-19 using CT images with the maximum lung regions in axial, coronal and sagittal views. The performance of the proposed model was evaluated by both the validation and testing sets.

RESULTS : The multi-view deep learning fusion model achieved the area under the receiver-operating characteristics curve (AUC) of 0.732, accuracy of 0.700, sensitivity of 0.730 and specificity of 0.615 in validation set. In the testing set, we can achieve AUC, accuracy, sensitivity and specificity of 0.819, 0.760, 0.811 and 0.615 respectively.

CONCLUSIONS : Based on deep learning method, the proposed diagnosis model trained on multi-view images of chest CT images showed great potential to improve the efficacy of diagnosis and mitigate the heavy workload of radiologists for the initial screening of COVID-19 pneumonia.

Wu Xiangjun, Hui Hui, Niu Meng, Li Liang, Wang Li, He Bingxi, Yang Xin, Li Li, Li Hongjun, Tian Jie, Zha Yunfei


Computed tomography, Coronavirus disease 2019, Deep learning, Multi-view model

General General

Trajectory-level fog detection based on in-vehicle video camera with TensorFlow deep learning utilizing SHRP2 naturalistic driving data.

In Accident; analysis and prevention

Providing drivers with real-time weather information and driving assistance during adverse weather, including fog, is crucial for safe driving. The primary focus of this study was to develop an affordable in-vehicle fog detection method, which will provide accurate trajectory-level weather information in real-time. The study used the SHRP2 Naturalistic Driving Study (NDS) video data and utilized several promising Deep Learning techniques, including Deep Neural Network (DNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN). Python programming on the TensorFlow Machine Learning library has been used for training the Deep Learning models. The analysis was done on a dataset consisted of three weather conditions, including clear, distant fog and near fog. During the training process, two optimizers, including Adam and Gradient Descent, have been used. While the overall prediction accuracy of the DNN, RNN, LSTM, and CNN using the Gradient Descent optimizer were found to be around 85 %, 77 %, 84 %, and 97 %, respectively; much improved overall prediction accuracy of 88 %, 91 %, 93 %, and 98 % for the DNN, RNN, LSTM, and CNN, respectively, were observed considering the Adam optimizer. The proposed fog detection method requires only a single video camera to detect weather conditions, and therefore, can be an inexpensive option to be fitted in maintenance vehicles to collect trajectory-level weather information in real-time for expanding as well as updating weather-based Variable Speed Limit (VSL) systems and Advanced Traveler Information Systems (ATIS).

Khan Md Nasim, Ahmed Mohamed M


Advanced Driver Assistance Systems, Advanced Travel Information Systems, Convolutional Neural Network, Deep Learning, Deep Neural Network, Foggy weather, Image Classification, Long Short-Term Memory, Machine Learning, Mobile Weather Sensors, Recurrent Neural Network, TensorFlow, Variable Speed Limit

General General

Direct determination of aberration functions in microscopy by an artificial neural network.

In Optics express

Adaptive optics relies on the fast and accurate determination of aberrations but is often hindered by wavefront sensor limitations or lengthy optimization algorithms. Deep learning by artificial neural networks has recently been shown to provide determination of aberration coefficients from various microscope metrics. Here we numerically investigate the direct determination of aberration functions in the pupil plane of a high numerical aperture microscope using an artificial neural network. We show that an aberration function can be determined from fluorescent guide stars and used to improve the Strehl ratio without the need for reconstruction from Zernike polynomial coefficients.

Cumming Benjamin P, Gu Min


General General

Detecting lane change maneuvers using SHRP2 naturalistic driving data: A comparative study machine learning techniques.

In Accident; analysis and prevention

Lane change has been recognized as a challenging driving maneuver and a significant component of traffic safety research. Developing a real-time continuous lane change detection system can assist drivers to perform and deal with complex driving tasks or provide assistance when it is needed the most. This study proposed trajectory-level lane change detection models based on features from vehicle kinematics, machine vision, roadway characteristics, and driver demographics under different weather conditions. To develop the models, the SHRP2 Naturalistic Driving Study (NDS) and Roadway Information Database (RID) datasets were utilized. Initially, descriptive statistics were utilized to investigate the lane change behavior, which revealed significant differences among different weather conditions for most of the parameters. Six data fusion categories were introduced for the first time, considering different data availability. In order to select relevant features in each category, Boruta, a wrapper-based algorithm was employed. The lane change detection models were trained, validated, and comparatively evaluated using four Machine Learning algorithms including Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and eXtrem Gradient Boosting (XGBoost). The results revealed that the highest overall detection accuracy was found to be 95.9 % using the XGBoost model when all the features were included in the model. Moreover, the highest overall detection accuracy of 81.9 % using the RF model was achieved considering only vehicle kinematics-based features, indicating that the proposed model could be utilized when other data are not available. Furthermore, the analysis of the impact of weather conditions on lane change detection suggested that incorporating weather could improve the accuracy of lane change detection. In addition, the analysis of early lane change detection indicated that the proposed algorithm could predict the lane changes within 5 s before the vehicles cross the lane line. The developed detection models could be used to monitor and control driver behavior in a Cooperative Automated Vehicle environment.

Das Anik, Khan Md Nasim, Ahmed Mohamed M


Artificial neural network, Connected vehicle, Lane change detection, Naturalistic driving study, Random Forest, Support vector machine, eXtrem gradient boosting