Combination Of Imaging Data Research Articles

Can we delineate brain injury in full-term neonates using serum biomarkers?

ABSTRACT OBJECTIVE: Early identification of neonates at risk of neurological impairment is particularly important for the bedside clinician. Clinical value of S100b and neuron-specific enolase in neonates has not been yet established. We investigated their kinetics and possible early clinical utility in neonatal encephalopathy (NE). STUDY DESIGN: 36 full-term neonates (13 with moderate/severe encephalopathy, 11 with mild encephalopathy, 12 controls) were enrolled and studied prospectively. Serum S100b and neuron-specific enolase (NSE) were measured serially on days(d) 1, 3, 9 and 18 of life. Brain MRI and long-term neurodevelopmental outcome were also assessed. RESULT: Neonates with moderate/severe encephalopathy had significantly increased S100b (d1) and NSE levels (d1, d3, d9) compared to controls. Neuron-specific enolase significantly correlated with the degree of encephalopathy, and a cutoff of 38.8 μg/l (d1) accurately predicted moderate/severe encephalopathy. S100b (d1) cutoff points of 1.6 μg/l and 11.4 μg/l prognosticated severe encephalopathy and death/cerebral palsy, respectively. Both biomarkers correlated well with neuroimaging and Bayley-III scores. CONCLUSION: Combined clinical, laboratory, imaging and neurodevelopmental data indicate that serum S100b and NSE can be useful biomarkers for the diagnosis and prognosis of neonatal brain injury, providing useful information to the bedside clinician.

Accessory pathway analysis using a multimodal deep learning model

Cardiac accessory pathways (APs) in Wolff–Parkinson–White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal model.

Potential Antiviral Target for SARS-CoV-2: A Key Early Responsive Kinase during Viral Entry

Currently, there is no effective antiviral medication for coronavirus disease 2019 (COVID-19) and the knowledge on the potential therapeutic target is in great need. Guided by a time-course transmi...

Ontology-guided segmentation and object identification for developmental mouse lung immunofluorescent images

BackgroundImmunofluorescent confocal microscopy uses labeled antibodies as probes against specific macromolecules to discriminate between multiple cell types. For images of the developmental mouse lung, these cells are themselves organized into densely packed higher-level anatomical structures. These types of images can be challenging to segment automatically for several reasons, including the relevance of biomedical context, dependence on the specific set of probes used, prohibitive cost of generating labeled training data, as well as the complexity and dense packing of anatomical structures in the image. The use of an application ontology helps surmount these challenges by combining image data with its metadata to provide a meaningful biological context, modeled after how a human expert would make use of contextual information to identify histological structures, that constrains and simplifies the process of segmentation and object identification.ResultsWe propose an innovative approach for the semi-supervised analysis of complex and densely packed anatomical structures from immunofluorescent images that utilizes an application ontology to provide a simplified context for image segmentation and object identification. We describe how the logical organization of biological facts in the form of an ontology can provide useful constraints that facilitate automatic processing of complex images. We demonstrate the results of ontology-guided segmentation and object identification in mouse developmental lung images from the Bioinformatics REsource ATlas for the Healthy lung database of the Molecular Atlas of Lung Development (LungMAP1) programConclusionWe describe a novel ontology-guided approach to segmentation and classification of complex immunofluorescence images of the developing mouse lung. The ontology is used to automatically generate constraints for each image based on its biomedical context, which facilitates image segmentation and classification.

Changes in chlorophyll a and its response to nitrogen and phosphorus characteristics over the past three decades in Poyang Lake, China

AbstractIn this study, we explore the relationship between changes in chlorophyll a (Chl a) content, total nitrogen (TN) and total phosphorus (TP) characteristics from 1989 to 2018 in Poyang Lake, China, by applying Landsat Thematic Mapper/Enhanced Thematic Mapper (TM/ETM) image data in combination with in situ monitoring data. Results showed that the average Chl a content in water over this 30‐year period was 5.77 μg/L. Although the water quality status of the lake was generally healthy, proportional Chl a content >10 μg/L (E10) has gradually increased, and the water quality in some areas of the lake has subsequently deteriorated over the past 30 years. Additionally, E10 was significantly positively correlated to the TN/TP. Changes in Chl a content were significantly positively correlated to the ratio of TN flux to TP flux from tributaries into the lake, but negatively correlated to TP flux. Results indicated that Lake can be categorized as phosphorus‐sufficient and nitrogen limited.

Machine Learning Analysis of Multispectral Imaging and Clinical Risk Factors to Predict Amputation Wound Healing

Machine Learning Analysis of Multispectral Imaging and Clinical Risk Factors to Predict Amputation Wound Healing

The International Radiomics Platform - An Initiative of the German and Austrian Radiological Societies - First Application Examples.

The DRG-ÖRG IRP (Deutsche Röntgengesellschaft-Österreichische Röntgengesellschaft international radiomics platform) represents a web-/cloud-based radiomics platform based on a public-private partnership. It offers the possibility of data sharing, annotation, validation and certification in the field of artificial intelligence, radiomics analysis, and integrated diagnostics. In a first proof-of-concept study, automated myocardial segmentation and automated myocardial late gadolinum enhancement (LGE) detection using radiomic image features will be evaluated for myocarditis data sets. The DRG-ÖRP IRP can be used to create quality-assured, structured image data in combination with clinical data and subsequent integrated data analysis and is characterized by the following performance criteria: Possibility of using multicentric networked data, automatically calculated quality parameters, processing of annotation tasks, contour recognition using conventional and artificial intelligence methods and the possibility of targeted integration of algorithms. In a first study, a neural network pre-trained using cardiac CINE data sets was evaluated for segmentation of PSIR data sets. In a second step, radiomic features were applied for segmental detection of LGE of the same data sets, which were provided multicenter via the IRP. First results show the advantages (data transparency, reliability, broad involvement of all members, continuous evolution as well as validation and certification) of this platform-based approach. In the proof-of-concept study, the neural network demonstrated a Dice coefficient of 0.813 compared to the expert's segmentation of the myocardium. In the segment-based myocardial LGE detection, the AUC was 0.73 and 0.79 after exclusion of segments with uncertain annotation.The evaluation and provision of the data takes place at the IRP, taking into account the FAT (fairness, accountability, transparency) and FAIR (findable, accessible, interoperable, reusable) criteria. It could be shown that the DRG-ÖRP IRP can be used as a crystallization point for the generation of further individual and joint projects. The execution of quantitative analyses with artificial intelligence methods is greatly facilitated by the platform approach of the DRG-ÖRP IRP, since pre-trained neural networks can be integrated and scientific groups can be networked.In a first proof-of-concept study on automated segmentation of the myocardium and automated myocardial LGE detection, these advantages were successfully applied.Our study shows that with the DRG-ÖRP IRP, strategic goals can be implemented in an interdisciplinary way, that concrete proof-of-concept examples can be demonstrated, and that a large number of individual and joint projects can be realized in a participatory way involving all groups. · The DRG-ÖRG IRP is a web/cloud-based radiomics platform based on a public-private partnership.. · The DRG-ÖRG IRP can be used for the creation of quality-assured, structured image data in combination with clinical data and subsequent integrated data analysis.. · First results show the applicability of left ventricular myocardial segmentation using a neural network and segment-based LGE detection using radiomic image features.. · The DRG-ÖRG IRP offers the possibility of integrating pre-trained neural networks and networking of scientific groups.. · Overhoff D, Kohlmann P, Frydrychowicz A et al. The International Radiomics Platform - An Initiative of the German and Austrian Radiological Societies. Fortschr Röntgenstr 2021; 193: 276 - 287.

Exploring the Limits of Combined Image/'omics Analysis for Non-cancer Histological Phenotypes.

The last several years have witnessed an explosion of methods and applications for combining image data with 'omics data, and for prediction of clinical phenotypes. Much of this research has focused on cancer histology, for which genetic perturbations are large, and the signal to noise ratio is high. Related research on chronic, complex diseases is limited by tissue sample availability, lower genomic signal strength, and the less extreme and tissue-specific nature of intermediate histological phenotypes. Data from the GTEx Consortium provides a unique opportunity to investigate the connections among phenotypic histological variation, imaging data, and 'omics profiling, from multiple tissue-specific phenotypes at the sub-clinical level. Investigating histological designations in multiple tissues, we survey the evidence for genomic association and prediction of histology, and use the results to test the limits of prediction accuracy using machine learning methods applied to the imaging data, genomics data, and their combination. We find that expression data has similar or superior accuracy for pathology prediction as our use of imaging data, despite the fact that pathological determination is made from the images themselves. A variety of machine learning methods have similar performance, while network embedding methods offer at best limited improvements. These observations hold across a range of tissues and predictor types. The results are supportive of the use of genomic measurements for prediction, and in using the same target tissue in which pathological phenotyping has been performed. Although this last finding is sensible, to our knowledge our study is the first to demonstrate this fact empirically. Even while prediction accuracy remains a challenge, the results show clear evidence of pathway and tissue-specific biology.

A Study on Positioning Algorithm for the Abnormal Temperature Area of Cotton Warehouses Based on Infrared Temperature Measurement and Visible Light Fusion

Aiming at the positioning problem in the fire emergency response of cotton warehouses, a positioning algorithm for the abnormal temperature area in cotton warehouses is proposed in this paper based on infrared temperature measurement and visible light fusion. This study combines image data fusion, temperature, and gas sensor data to detect the abnormal temperature area, and positions the spatial three-dimensional point through camera calibration and pose estimation, to achieve early warning and emergency response of cotton warehouses. The experiments show that this algorithm can effectively achieve the positioning of the abnormal temperature area.

TOWARDS DETECTION OF THERMAL ANOMALIES IN LARGE URBAN AREAS USING SIMULATION

Abstract. Anomaly detection in imagery has widely been studied and enhanced towards the requirements of today’s available sensor data, whereas many of them require a background estimation in order to identify an anomaly or target. In this paper, we examine an analysis of simulation as background estimator for anomaly detection in thermal images of urban sceneries. We generate a surface temperature image and a sensor-like infrared image by combined image and elevation data and a thermal model suited for large scenes and fast simulation. With the simulated thermal image, we define anomalies as deviation between measurement and simulation. Pixel-wise image differencing of the measured and simulated temperatures and infrared images respectively are performed and evaluated concerning the full images as well as class-wise, including a material classification of the observed area. Our approach shows complementary results compared to RXD application on the measured infrared images. Metal roofs which appear warm in the thermal image and are not visually distinguishable from the residual image are detected.

Structural and hemodynamic properties of murine pulmonary arterial networks under hypoxia-induced pulmonary hypertension.

Detection and monitoring of patients with pulmonary hypertension, defined as a mean blood pressure in the main pulmonary artery above 25 mmHg, requires a combination of imaging and hemodynamic measurements. This study demonstrates how to combine imaging data from microcomputed tomography images with hemodynamic pressure and flow waveforms from control and hypertensive mice. Specific attention is devoted to developing a tool that processes computed tomography images, generating subject-specific arterial networks in which one-dimensional fluid dynamics modeling is used to predict blood pressure and flow. Each arterial network is modeled as a directed graph representing vessels along the principal pathway to ensure perfusion of all lobes. The one-dimensional model couples these networks with structured tree boundary conditions representing the small arteries and arterioles. Fluid dynamics equations are solved in this network and compared to measurements of pressure in the main pulmonary artery. Analysis of microcomputed tomography images reveals that the branching ratio is the same in the control and hypertensive animals, but that the vessel length-to-radius ratio is significantly lower in the hypertensive animals. Fluid dynamics predictions show that in addition to changed network geometry, vessel stiffness is higher in the hypertensive animal models than in the control models.

Cross-modal image fusion guided by subjective visual attention

The human visual perception system has very strong robustness and contextual awareness in a variety of image processing tasks. This robustness and the perception ability of contextual awareness is closely related to the characteristics of multi-task auxiliary learning and subjective attention of the human visual perception system. In order to improve the robustness and contextual awareness of image fusion tasks, we proposed a multi-task auxiliary learning image fusion method guided by subjective attention. The image fusion method effectively unifies the subjective task intention and prior knowledge of human brain. In order to achieve our proposed image fusion method, we first analyze the mechanism of multi-task auxiliary learning, build a multi-task auxiliary learning network. Secondly, based on the human visual attention perception mechanism, we introduce the human visual attention network guided by subjective tasks on the basis of the multi-task auxiliary learning network. The subjective intention is introduced by the subjective attention task model, so that the network can fuse images according to the subjective intention. Finally, in order to verify the superiority of our image fusion method, we carried out experiments on the combined vision system image data set, and the infrared and visible image data set for experimental verification. The experimental results demonstrate the superiority of our fusion method over state-of-arts in contextual awareness and robustness.

Towards Haptic Images: A Survey on Touchscreen-Based Surface Haptics.

The development of tactile screens opens new perspectives for co-located images and haptic rendering, leading to the concept of "haptic images." They emerge from the combination of image data, rendering hardware, and haptic perception. This enables one to perceive haptic feedback while manually exploring an image. This raises nevertheless two scientific challenges, which serve as thematic axes for the state of the art of this survey. Firstly, the choice of appropriate haptic data raises a number of issues about human perception, measurements, modeling and distribution. Secondly, the choice of appropriate rendering technology implies a difficult trade-off between expressiveness and usability.

Comparing dynamic causal models of neurovascular coupling with fMRI and EEG/MEG

This technical note presents a dynamic causal modelling (DCM) procedure for evaluating different models of neurovascular coupling in the human brain – using combined electromagnetic (M/EEG) and functional magnetic resonance imaging (fMRI) data. This procedure compares the evidence for biologically informed models of neurovascular coupling using Bayesian model comparison. First, fMRI data are used to localise regionally specific neuronal responses. The coordinates of these responses are then used as the location priors in a DCM of electrophysiological responses elicited by the same paradigm. The ensuing estimates of model parameters are then used to generate neuronal drive functions, which model pre- or post-synaptic activity for each experimental condition. These functions form the input to a model of neurovascular coupling, whose parameters are estimated from the fMRI data. Crucially, this enables one to evaluate different models of neurovascular coupling, using Bayesian model comparison – asking, for example, whether instantaneous or delayed, pre- or post-synaptic signals mediate haemodynamic responses. We provide an illustrative application of the procedure using a single-subject auditory fMRI and MEG dataset. The code and exemplar data accompanying this technical note are available through the statistical parametric mapping (SPM) software.

DeepBTS: Prediction of Recurrence-free Survival of Non-small Cell Lung Cancer Using a Time-binned Deep Neural Network

Accurate prediction of non-small cell lung cancer (NSCLC) prognosis after surgery remains challenging. The Cox proportional hazard (PH) model is widely used, however, there are some limitations associated with it. In this study, we developed novel neural network models called binned time survival analysis (DeepBTS) models using 30 clinico-pathological features of surgically resected NSCLC patients (training cohort, n = 1,022; external validation cohort, n = 298). We employed the root-mean-square error (in the supervised learning model, s- DeepBTS) or negative log-likelihood (in the semi-unsupervised learning model, su-DeepBTS) as the loss function. The su-DeepBTS algorithm achieved better performance (C-index = 0.7306; AUC = 0.7677) than the other models (Cox PH: C-index = 0.7048 and AUC = 0.7390; s-DeepBTS: C-index = 0.7126 and AUC = 0.7420). The top 14 features were selected using su-DeepBTS model as a selector and could distinguish the low- and high-risk groups in the training cohort (p = 1.86 × 10−11) and validation cohort (p = 1.04 × 10−10). When trained with the optimal feature set for each model, the su-DeepBTS model could predict the prognoses of NSCLC better than the traditional model, especially in stage I patients. Follow-up studies using combined radiological, pathological imaging, and genomic data to enhance the performance of our model are ongoing.

Influence of Gender and Environment on the Occurrence of Chronic Obstructive Pulmonary Disease: Cross-Sectional Study

<i>Background and Objective</i>: Chronic obstructive pulmonary disease (COPD) is predicted to be the third leading cause of death worldwide by 2020. Its diagnosis remains a challenge in developing countries such as DRC, with the use of Gold standard, spirometry, limited. Chest imaging plays an important role in orientation. The absence of local radiological data from COPD had therefore motivated this study. The aim of this study is to establish a relationship between gender, environment and COPD. <i>Materials and Methods</i>: Retrospective and analytical study of clinical and thoracic imaging data (radiography and CT scan), collected from the records of 120 COPD subjects followed in three Kinshasa medical trainings between January 2014 and June 2017. Fisher's test compared the results obtained. The combination of imaging data and clinical phenotype through Pearson chi-square testing, logistic regression and odds-ratio (OR). The service threshold was set at 0.05. <i>Results</i>: the study population (average age of 64.52±6.82 years) was predominantly male (78.3% n=94). <i>Results</i>: the study population (average age of 64.52±6.82 years) was predominantly male (78.3% n=94). The proportions of sputum were more common in men than in women, the difference being statistically significant. Indeed, the male sex confers a triple risk (OR=3.1; IC 95% 1.2-8; P=0.015). Women were more exposed to domestic and/or occupational pollution (65.4% n=17; than men were (26.6% n=25). Indeed, the male sex conferred a risk of exposure to domestic pollution and/or occupational pollution multiplied by 14 times (OR=14.3; IC 95%; 2-100; P=0.001) and allergy conferred a risk of exposure to domestic pollution by 17 times (OR=17.1; IC95%: 2.1-137, p=0.007). <i>Conclusion</i>: This study showed the male sex and allergy is conferred a high risk of the domestic pollution or occupation pollution.

Multi-factorial age estimation: A Bayesian approach combining dental and skeletal magnetic resonance imaging

PurposeTo study age estimation performance of combined magnetic resonance imaging (MRI) data of all four third molars, the left wrist and both clavicles in a reference population of females and males. To study the value of adding anthropometric and sexual maturation data. Materials and methodsThree Tesla MRI of the three anatomical sites was prospectively conducted from March 2012 to May 2017 in 14- to 26-year-old healthy Caucasian volunteers (160 females, 138 males). Development was assessed by allocating stages, anthropometric measurements were taken, and self-reported sexual maturation data were collected. All data was incorporated in a continuation-ratio model to estimate age, applying Bayes’ rule to calculate point and interval predictions. Two performance aspects were studied: (1) accuracy and uncertainty of the point prediction, and (2) diagnostic ability to discern minors from adults (≥18 years). ResultsCombining information from different anatomical sites decreased the mean absolute error (MAE) compared to incorporating only one site (P<0.0001). By contrast, adding anthropometric and sexual maturation data did not further improve MAE (P=0.11). In females, combining all three anatomical sites rendered a MAE equal to 1.41 years, a mean width of the 95% prediction intervals of 5.91 years, 93% correctly classified adults and 91% correctly classified minors. In males, the corresponding results were 1.36 years, 5.49 years, 94%, and 90%, respectively. ConclusionAll aspects of age estimation improve when multi-factorial MRI data of the three anatomical sites are incorporated. Anthropometric and sexual maturation data do not seem to add relevant information.

Subkiloparsec Imaging of Lyα Emission in a Low-mass, Highly Ionized, Gravitationally Lensed Galaxy at z = 1.84∗

Abstract Low-mass, low-metallicity galaxies at low to moderate (z ≲ 3) redshifts offer the best opportunity for detailed examination of the interplay between massive stars, ionizing radiation and gas in sources similar to those that likely reionized the universe. We present new narrowband Hubble Space Telescope observations of emission and the adjacent ultraviolet (UV) continuum in the low-mass (M ⋆ = 2 × 108 ), low-metallicity (Z ∼ 1/20 Z⊙), and highly ionized gravitationally lensed galaxy SL2S J02176−0513 at z = 1.844. The galaxy has strong emission with photometric equivalent width , at odds with the escape fraction of 10%. However, the spectroscopic profile suggests the presence of broad absorption underlying the emission, and the total equivalent width is consistent with the escape fraction once this underlying absorption is included. The emission is more spatially extended than the UV continuum, and the 0.″14 spatial resolution of HST coupled with the magnification of gravitational lensing enables us to examine the distribution of and the UV continuum on subkiloparsec scales. We find that the peaks of the emission and the UV continuum are offset by 650 pc, and there is no emission arising from the region with the strongest UV light. Our combined spectroscopic and imaging data imply a significant range in neutral hydrogen column density across the object. These observations offer indirect support for a model in which ionizing radiation escapes from galaxies through channels with low column density of neutral gas.

Evaluating Invasive EEG Implantations with Structural Imaging Data and Functional Scalp EEG Recordings from Epilepsy Patients.

Seizures in patients with medically refractory epilepsy (MRE) cannot be controlled with drugs. For focal MRE, seizures originate in the epileptogenic zone (EZ), which is the minimum amount of cortex that must be treated to be seizure free. Localizing the EZ is often a laborious process wherein clinicians first inspect scalp EEG recordings during several seizure events, and then formulate an implantation plan for subsequent invasive monitoring. The goal of implantation is to place electrodes into the brain region covering the EZ. Then, during invasive monitoring, clinicians visually inspect intracranial EEG recordings to more precisely localize the EZ. Finally, the EZ is then surgically ablated, removed or treated with electrical stimulation. Unfortunately success rates average at 50%. Such grim outcomes call for analytical assistance in creating more accurate implantation plans from scalp EEG. In this paper, we introduce a method that combines imaging data (CT and MRI scans) with scalp EEG to derive an implantation distribution. Specifically, scalp EEG data recorded over a seizure event is converted into a time-gamma frequency map, which is then processed to derive a spectrally annotated implantation distribution (SAID). The SAID represents a distribution of gamma power in each of eight cortical lobe/hemisphere partitions. We applied this method to 4 MRE patients who underwent treatment, and found that the SAID distribution overlapped more with clinical implantations in success cases than in failed cases. These preliminary findings suggest that the SAID may help in improving EZ localization accuracy and surgical outcomes.

The oxytocin receptor gene predicts brain activity during an emotion recognition task in autism

BackgroundAutism is a highly varied and heritable neurodevelopmental condition, and common variants explain approximately 50% of the genetic variance of autism. One of the genes implicated in autism is the oxytocin receptor (OXTR). The current study combined genetic and brain imaging (fMRI) data to examine the moderating effect of genotype on the association between diagnosis and brain activity in response to a test of cognitive empathy.MethodsParticipants were adolescents (mean age = 14.7 ± 1.7) who were genotyped for single nucleotide polymorphisms (SNPs) within the OXTR and underwent functional brain imaging while completing the adolescent version of the ‘Reading the Mind in the Eyes’ Test (Eyes Test).ResultsTwo (rs2254298, rs53576) of the five OXTR SNPs examined were significantly associated with brain activity during the Eyes Test, and three of the SNPs (rs2254298, rs53576, rs2268491) interacted with diagnostic status to predict brain activity. All of the effects localized to the right supramarginal gyrus (rSMG) and an overlap analysis revealed a large overlap of the effects. An exploratory analysis showed that activity within an anatomically defined rSMG and genotype can predict diagnostic status with reasonable accuracy.ConclusionsThis is one of the first studies to investigate OXTR and brain function in autism. The findings suggest a neurogenetic mechanism by which OXTR-dependent activity within the rSMG is related to the aetiology of autism.