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.
Artificial prosthesis is an important tool to help amputees to gain or partially obtain abled human limb functions. Compared with traditional prosthesis which is only for decoration or merely has feedforward control channel, the perception and feedback function of prosthesis is an important guarantee for its normal use and self-safety. And this includes the information of position, force, texture, roughness, temperature and so on. This paper mainly summarizes the development and current status of artificial prostheses in the field of perception and feedback technology in recent years, which is derived from two aspects: the recognition way of perception signals and the feedback way of perception signals. Among the part of recognition way of perception signals, the current commonly adopted sensors related to perception information acquisition and their application status in prosthesis are overviewed. Additionally, from the aspects of force feedback stimulation, invasive/non-invasive electrical stimulation, and vibration stimulation, the feedback methods of perception signals are summarized and analyzed. Finally, some problems existing in the perception and feedback technology of artificial prosthesis are proposed, and their development trends are also prospected.
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.
Ultrasound diffraction tomography (UDT) possesses the characteristics of high resolution, sensitive to dense tissue, and has high application value in clinics. To suppress the artifact and improve the quality of reconstructed image, classical interpolation method needs to be improved by increasing the number of projections and channels, which will increase the scanning time and the complexity of the imaging system. In this study, we tried to accurately reconstruct the object from limited projection based on compressed sensing. Firstly, we illuminated the object from random angles with limited number of projections. Then we obtained spatial frequency samples through Fourier diffraction theory. Secondly, we formulated the inverse problem of UDT by exploring the sparsity of the object. Thirdly, we solved the inverse problem by conjugate gradient method to reconstruct the object. We accurately reconstructed the object using the proposed method. Not only can the proposed method save scanning time to reduce the distortion by respiratory movement, but also can reduce cost and complexity of the system. Compared to the interpolation method, our method can reduce the reconstruction error and improve the structural similarity.
Objective To investigate the level and influencing factors of perceived HIV stigma and discrimination among people living with HIV/AIDS (PLWHA). Methods By using convenience sampling method, 123 patients were recruited from the department of infectious diseases in a tertiary hospital in Chengdu from April to May in 2017. Berger HIV stigma scale was used to measure the level of perceived HIV stigma. Results The mean score of Berger HIV stigma scale was 113.72±17.890, which revealed a middle to upper level. Among the four subscales, the score of disclosure concerns (3.07±0.462) was the highest, while the score of negative self-image (2.70±0.494) was the lowest. Multiple regression analysis showed that gender and self-perceived health status were the influencing factors of perceived HIV stigma. Conclusions The level of perceived HIV stigma among PLWHA is from middle to upper level. Female gender and poor self-perceived health status are associated with a higher level of perceived HIV stigma. Individualized interventions are required in order to reduce the level of HIV stigma.
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.
ObjectiveTo investigate the differences in self-perception level of asthma control and the factors affecting the ability of self-perception in patients with bronchial asthma. MethodsA total of 322 patients who were diagnosed with bronchial asthma at the First Affiliated Hospital of Harbin Medical University from March 2013 to February 2015 were recruited in the study. The clinical data were collected, including the demographic characteristics of the patients, the Asthma Control Test (ACT) and results of routine blood test and pulmonary function test on the same day that they were required to fill out the ACT. Then they were followed up at the 1st, 3rd, 6th, 12th months, and required to fill out the ACT again, and underwent the blood routine test and lung function test. In addition, health education about asthma was offered regularly during these visits. ResultsA total of 226 patients met the inclusion criteria of the study. The patients with asthma had significant differences between self-perception control level and real symptoms control level (P<0.05). The patients who were 65 years old or older perceived their symptoms of bronchial asthma rather poorly (P=0.000). The patients who received senior high school or higher education had a higher ability of self-perceived asthma control (P=0.005). The patients with allergic rhinitis combined were less likely to correctly perceive their illness compared with those who did not suffered from allergic rhinitis, and the difference was statistically significant (P=0.001). In addition, for those diagnosed with allergic rhinitis combined with bronchial asthma, regular treatment also made difference--longer treatment for rhinitis means a higher ability of self-perceived asthma control (P=0.000). The health education did play a constructive role in helping patients correctly perceive their illness (P=0.000). There was no correlation between the absolute value of peripheral blood eosinophils and the accuracy of self-perceived asthma control. Nevertheless,there was a noticeable correlation between the ability of peripheral blood eosinophils of patients with asthma and acute attack of bronchial asthma (P=0.003),which was a meaningful finding in assessing the risk of future acute attack of bronchial asthma (P=0.469). ConclusionsThere is a significant difference between self-perception control level and symptom control level in patients with asthma. The self-perception control level of asthma patients who are elderly, the low degree of educational level, merged allergic rhinitis, and lack of health education are associated with lower accuracy of self-perception control level. The absolute value of peripheral blood eosinophils of the patients with asthma can be used to assess the risk of asthma acute attack in the future, but has no significant correlation with the accuracy of self-perception control level.
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.
In deep learning-based image registration, the deformable region with complex anatomical structures is an important factor affecting the accuracy of network registration. However, it is difficult for existing methods to pay attention to complex anatomical regions of images. At the same time, the receptive field of the convolutional neural network is limited by the size of its convolution kernel, and it is difficult to learn the relationship between the voxels with far spatial location, making it difficult to deal with the large region deformation problem. Aiming at the above two problems, this paper proposes a cascaded multi-level registration network model based on transformer, and equipped it with a difficult deformable region perceptron based on mean square error. The difficult deformation perceptron uses sliding window and floating window techniques to retrieve the registered images, obtain the difficult deformation coefficient of each voxel, and identify the regions with the worst registration effect. In this study, the cascaded multi-level registration network model adopts the difficult deformation perceptron for hierarchical connection, and the self-attention mechanism is used to extract global features in the basic registration network to optimize the registration results of different scales. The experimental results show that the method proposed in this paper can perform progressive registration of complex deformation regions, thereby optimizing the registration results of brain medical images, which has a good auxiliary effect on the clinical diagnosis of doctors.
In order to address the issues of spatial induction bias and lack of effective representation of global contextual information in colon polyp image segmentation, which lead to the loss of edge details and mis-segmentation of lesion areas, a colon polyp segmentation method that combines Transformer and cross-level phase-awareness is proposed. The method started from the perspective of global feature transformation, and used a hierarchical Transformer encoder to extract semantic information and spatial details of lesion areas layer by layer. Secondly, a phase-aware fusion module (PAFM) was designed to capture cross-level interaction information and effectively aggregate multi-scale contextual information. Thirdly, a position oriented functional module (POF) was designed to effectively integrate global and local feature information, fill in semantic gaps, and suppress background noise. Fourthly, a residual axis reverse attention module (RA-IA) was used to improve the network’s ability to recognize edge pixels. The proposed method was experimentally tested on public datasets CVC-ClinicDB, Kvasir, CVC-ColonDB, and EITS, with Dice similarity coefficients of 94.04%, 92.04%, 80.78%, and 76.80%, respectively, and mean intersection over union of 89.31%, 86.81%, 73.55%, and 69.10%, respectively. The simulation experimental results show that the proposed method can effectively segment colon polyp images, providing a new window for the diagnosis of colon polyps.