Taking advantages of the sparsity or compressibility inherent in real world signals, compressed sensing (CS) can collect compressed data at the sampling rate much lower than that needed in Shannon’s theorem. The combination of CS and low rank modeling is used to medical imaging techniques to increase the scanning speed of cardiac magnetic resonance (CMR), alleviate the patients’ suffering and improve the images quality. The alternating direction method of multipliers (ADMM) algorithm is proposed for multiscale low rank matrix decomposition of CMR images. The algorithm performance is evaluated quantitatively by the peak signal to noise ratio (PSNR) and relative l2 norm error (RLNE), with the human visual system and the local region magnification as the qualitative comparison. Compared to L + S, kt FOCUSS, k-t SPARSE SENSE algorithms, experimental results demonstrate that the proposed algorithm can achieve the best performance indices, and maintain the most detail features and edge contours. The proposed algorithm can encourage the development of fast imaging techniques, and improve the diagnoses values of CMR in clinical applications.
Organoids are an in vitro model that can simulate the complex structure and function of tissues in vivo. Functions such as classification, screening and trajectory recognition have been realized through organoid image analysis, but there are still problems such as low accuracy in recognition classification and cell tracking. Deep learning algorithm and organoid image fusion analysis are the most advanced organoid image analysis methods. In this paper, the organoid image depth perception technology is investigated and sorted out, the organoid culture mechanism and its application concept in depth perception are introduced, and the key progress of four depth perception algorithms such as organoid image and classification recognition, pattern detection, image segmentation and dynamic tracking are reviewed respectively, and the performance advantages of different depth models are compared and analyzed. In addition, this paper also summarizes the depth perception technology of various organ images from the aspects of depth perception feature learning, model generalization and multiple evaluation parameters, and prospects the development trend of organoids based on deep learning methods in the future, so as to promote the application of depth perception technology in organoid images. It provides an important reference for the academic research and practical application in this field.
The construction of brain functional network based on resting-state functional magnetic resonance imaging (fMRI) is an effective method to reveal the mechanism of human brain operation, but the common brain functional network generally contains a lot of noise, which leads to wrong analysis results. In this paper, the least absolute shrinkage and selection operator (LASSO) model in compressed sensing is used to reconstruct the brain functional network. This model uses the sparsity of L1-norm penalty term to avoid over fitting problem. Then, it is solved by the fast iterative shrinkage-thresholding algorithm (FISTA), which updates the variables through a shrinkage threshold operation in each iteration to converge to the global optimal solution. The experimental results show that compared with other methods, this method can improve the accuracy of noise reduction and reconstruction of brain functional network to more than 98%, effectively suppress the noise, and help to better explore the function of human brain in noisy environment.
Medical visual question answering (MVQA) plays a crucial role in the fields of computer-aided diagnosis and telemedicine. Due to the limited size and uneven annotation quality of the MVQA datasets, most existing methods rely on additional datasets for pre-training and use discriminant formulas to predict answers from a predefined set of labels. This approach makes the model prone to overfitting in low resource domains. To cope with the above problems, we propose an image-aware generative MVQA method based on image caption prompts. Firstly, we combine a dual visual feature extractor with a progressive bilinear attention interaction module to extract multi-level image features. Secondly, we propose an image caption prompt method to guide the model to better understand the image information. Finally, the image-aware generative model is used to generate answers. Experimental results show that our proposed method outperforms existing models on the MVQA task, realizing efficient visual feature extraction, as well as flexible and accurate answer outputs with small computational costs in low-resource domains. It is of great significance for achieving personalized precision medicine, reducing medical burden, and improving medical diagnosis efficiency.
Electric and electronic products are required to pass through the certification on electrical safety performance before entering into the market in order to reduce electrical shock and electrical fire so as to protect the safety of people and property. The leakage current is the most important factor in testing the electrical safety performance and the test theory is based on the perception current effect and threshold. The traditional method testing the current threshold for perception only depends on the sensing of the human body and is affected by psychological factors. Some authors filter the effect of subjective sensation by using physiological and psychological statistical algorithm in recent years and the reliability and consistency of the experiment data are improved. We established an experiment system of testing the human body's current threshold for perception based on EEG feature analysis, and obtained 967 groups of data. We used wavelet packet analysis to detect α wave from EEG, and used FFT to do spectral analysis on α wave before and after the current flew through the human body. The study has shown that about 97.72% α wave energy changes significantly when electrical stimulation occurs. It is well proved that when the EEG feature identification is applied to test the human body current threshold for perception, and meanwhile α wave energy change and human body sensing are used together to confirm if the current flowing through the human body reaches the perception threshold, the measurement of the human body current threshold for perception could be carried out objectively and accurately.
As the most efficient perception system in nature, the perception mechanism of the insect (such as honeybee) antennae is the key to imitating the high-performance sensor technology. An automated experimental device suitable for collecting electrical signals (including antenna reaction time information) of antennae was developed, in response to the problems of the non-standardized experimental process, interference of manual operation, and low efficiency in the study of antenna perception mechanism. Firstly, aiming at the automatic identification and location of insect heads in experiments, the image templates of insect head contour features were established. Insect heads were template-matched based on the Hausdorff method. Then, for the angle deviation of the insect heads relative to the standard detection position, a method that calculates the angle of the insect head mid-axis based on the minimum external rectangle of the long axis was proposed. Eventually, the electrical signals generated by the antennae in contact with the reagents were collected by the electrical signal acquisition device. Honeybees were used as the research object in this study. The experimental results showed that the accuracy of template matching could reach 95.3% to locate the bee head quickly, and the deviation angle of the bee head was less than 1°. The distance between antennae and experimental reagents could meet the requirements of antennae perception experiments. The parameters, such as the contact reaction time of honeybee antennae to sucrose solution, were consistent with the results of the manual experiment. The system collects effectively antenna contact signals in an undisturbed state and realizes the standardization of experiments on antenna perception mechanisms, which provides an experimental method and device for studying and analyzing the reaction time of the antenna involved in biological antenna perception mechanisms.
ObjectiveTo reveal impairments in the perceptual networks in tuberous sclerosis complex (TSC) with epilepsy by functional connectivity MRI (fcMRI). MethodsThe fcMRI-based independent component analysis (ICA) was used to measure the resting state functional connectivity in nine TSC patients with epilepsy recruited from June 2010 to June 2012 and perceptual networks including the sensorimotor network (SMN), visual network (VN), and auditory network (AN) were investigated. The correlation between Z values in regions of interest (ROIs) and age of seizure onset or duration of epilepsy were analyzed. ResultsCompared with the controls, the TSC patients with epilepsy presented decreased functional connectivity in primary visual cortex within the VN networks and there were no increased connectivity. Increased connectivity in left middle temporal gyrus and inferior temporal gyrus was found and decreased connectivity was detected in right inferior frontal gyrus within AN networks. Decreased connectivity was detected at the right inferior frontal gyrus and the increase in connectivity was found in right thalamus within SMN netwoks. No significant correlations were found between Z values in ROIs including the primary visual cortex within the VN, right thalamus and inferior frontal gyrus within SMN, left temporal lobe and right inferior frontal gyrus within AN and the duration of the disease or the age of onset. ConclusionFhere is altered (both increased and decreased) functional connectivity in the perceptual networks of TSC patients with epilepsy. The decreased functional connectivity may reflect the dysfunction of correlative perceptual networks in TSC patients, and the increased functional connectivity may indicate the compensatory mechanism or reorganization of cortical networks. Our fcMRI study may contribute to the understanding of neuropathophysiological mechanisms underlying perceptual impairments in TSC patients with epilepsy.
Multi-layer perceptron (MLP) neural network belongs to multi-layer feedforward neural network, and has the ability and characteristics of high intelligence. It can realize the complex nonlinear mapping by its own learning through the network. Bipolar disorder is a serious mental illness with high recurrence rate, high self-harm rate and high suicide rate. Most of the onset of the bipolar disorder starts with depressive episode, which can be easily misdiagnosed as unipolar depression and lead to a delayed treatment so as to influence the prognosis. The early identification of bipolar disorder is of great importance for patients with bipolar disorder. Due to the fact that the process of early identification of bipolar disorder is nonlinear, we in this paper discuss the MLP neural network application in early identification of bipolar disorder. This study covered 250 cases, including 143 cases with recurrent depression and 107 cases with bipolar disorder, and clinical features were statistically analyzed between the two groups. A total of 42 variables with significant differences were screened as the input variables of the neural network. Part of the samples were randomly selected as the learning sample, and the other as the test sample. By choosing different neural network structures, all results of the identification of bipolar disorder were relatively good, which showed that MLP neural network could be used in the early identification of bipolar disorder.