A novel higher order appearance texture analysis to diagnose lung cancer based on a modified local ternary pattern

BACKGROUND AND OBJECTIVE

Lung cancer is an important cause of death and morbidity around the world. Two of the primary computed tomography (CT) imaging markers that can be used to differentiate malignant and benign lung nodules are the inhomogeneity of the nodules' texture and nodular morphology. The objective of this paper is to present a new model that can capture the inhomogeneity of the detected lung nodules as well as their morphology.

METHODS

We modified the local ternary pattern to use three different levels (instead of two) and a new pattern identification algorithm to capture the nodule's inhomogeneity and morphology in a more accurate and flexible way. This modification aims to address the wide Hounsfield unit value range of the detected nodules which decreases the ability of the traditional local binary/ternary pattern to accurately classify nodules' inhomogeneity. The cut-off values defining these three levels of the novel technique are estimated empirically from the training data. Subsequently, the extracted imaging markers are fed to a hyper-tuned stacked generalization-based classification architecture to classify the nodules as malignant or benign. The proposed system was evaluated on in vivo datasets of 679 CT scans (364 malignant nodules and 315 benign nodules) from the benchmark Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) and an external dataset of 100 CT scans (50 malignant and 50 benign). The performance of the classifier was quantitatively assessed using a Leave-one-out cross-validation approach and externally validated using the unseen external dataset based on sensitivity, specificity, and accuracy.

RESULTS

The overall accuracy of the system is 96.17% with 97.14% sensitivity and 95.33% specificity. The area under the receiver-operating characteristic curve was 0.98, which highlights the robustness of the system. Using the unseen external dataset for validating the system led to consistent results showing the generalization abilities of the proposed approach. Moreover, applying the original local binary/ternary pattern or using other classification structures achieved inferior performance when compared against the proposed approach.

CONCLUSIONS

These experimental results demonstrate the feasibility of the proposed model as a novel tool to assist physicians and radiologists for lung nodules' early assessment based on the new comprehensive imaging markers.

Adding Sound Transparency to a Spacesuit: Effect on Cognitive Performance in Females

Spacesuits may block external sound. This induces sensory deprivation; a side effect is lower cognitive performance. This can increase the risk of an accident. This undesirable effect can be mitigated by designing suits with sound transparency. If the atmosphere is available, as on Mars, sound transparency can be realized by augmenting and processing external sounds. If no atmosphere is available, such as on the Moon, then an Earth-like sound can be re-created via generative AR techniques. We measure the effect of adding sound transparency in an Intra-Vehicular Activity suit by means of the Koh Block test. The results indicate that participants complete the test more quickly when wearing a suit with sound transparency.

Design and Analysis of Circular Polarized Two-Port MIMO Antennas with Various Antenna Element Orientations

This article presents the circularly polarized antenna operating over 28 GHz mm-wave applications. The suggested antenna has compact size, simple geometry, wideband, high gain, and offers circular polarization. Afterward, two-port MIMO antenna are designed to get Left Hand Circular Polarization (LHCP) and Right-Hand Circular Polarization (RHCP). Four different cases are adopted to construct two-port MIMO antenna of suggested antenna. In case 1, both of the elements are placed parallel to each other; in the second case, the element is parallel but the radiating patch of second antenna element are rotated by 180°. In the third case, the second antenna element is placed orthogonally to the first antenna element. In the final case, the antenna is parallel but placed in the opposite end of substrate material. The S-parameters, axial ratio bandwidth (ARBW) gain, and radiation efficiency are studied and compared in all these cases. The two MIMO systems of all cases are designed by using Roger RT/Duroid 6002 with thickness of 0.79 mm. The overall size of two-port MIMO antennas is 20.5 mm × 12 mm × 0.79 mm. The MIMO configuration of the suggested CP antenna offers wideband, low mutual coupling, wide ARBW, high gain, and high radiation efficiency. The hardware prototype of all cases is fabricated to verify the predicated results. Moreover, the comparison of suggested two-port MIMO antenna is also performed with already published work, which show the quality of suggested work in terms of various performance parameters over them.

Efficient framework for brain tumor detection using different deep learning techniques

The brain tumor is an urgent malignancy caused by unregulated cell division. Tumors are classified using a biopsy, which is normally performed after the final brain surgery. Deep learning technology advancements have assisted the health professionals in medical imaging for the medical diagnosis of several symptoms. In this paper, transfer-learning-based models in addition to a Convolutional Neural Network (CNN) called BRAIN-TUMOR-net trained from scratch are introduced to classify brain magnetic resonance images into tumor or normal cases. A comparison between the pre-trained InceptionResNetv2, Inceptionv3, and ResNet50 models and the proposed BRAIN-TUMOR-net is introduced. The performance of the proposed model is tested on three publicly available Magnetic Resonance Imaging (MRI) datasets. The simulation results show that the BRAIN-TUMOR-net achieves the highest accuracy compared to other models. It achieves 100%, 97%, and 84.78% accuracy levels for three different MRI datasets. In addition, the k-fold cross-validation technique is used to allow robust classification. Moreover, three different unsupervised clustering techniques are utilized for segmentation.

Predicting the Level of Respiratory Support in COVID-19 Patients Using Machine Learning

In this paper, a machine learning-based system for the prediction of the required level of respiratory support in COVID-19 patients is proposed. The level of respiratory support is divided into three classes: class 0 which refers to minimal support, class 1 which refers to non-invasive support, and class 2 which refers to invasive support. A two-stage classification system is built. First, the classification between class 0 and others is performed. Then, the classification between class 1 and class 2 is performed. The system is built using a dataset collected retrospectively from 3491 patients admitted to tertiary care hospitals at the University of Louisville Medical Center. The use of the feature selection method based on analysis of variance is demonstrated in the paper. Furthermore, a dimensionality reduction method called principal component analysis is used. XGBoost classifier achieves the best classification accuracy (84%) in the first stage. It also achieved optimal performance in the second stage, with a classification accuracy of 83%.

Impact of stress and hypertension on the cerebrovasculature

OBJECTIVES

Both stress and hypertension (HTN) are considered major health problems that negatively impact the cerebral vasculature. In this article we summarize the possible relationship between stress and HTN.

METHODS

We conducted a systematic review of the literature using a database search of MEDLINE, PubMed, Scopus, and Web of Science.

RESULTS

Psychological stress is known to be an important risk factor for essential hypertension. Acute stress can induce transient elevations of blood pressure in the context of the fight-or-flight response. With increased intensity and duration of a perceived harmful event, the normal physiological response is altered, resulting in a failure to return to the resting levels. These changes are responsible for the development of HTN. Genetic and behavioral factors are also very important for the pathogenesis of hypertension under chronic stress situation. In addition, HTN and chronic stress may lead to impaired auto-regulation, regional vascular remodeling, and breakdown of the blood brain barrier (BBB). The effects of both HTN and chronic stress on the cerebral blood vessels shows that both have common structural and functional effects including endothelial damage with subsequent increased wall thickness, vessel resistance, stiffness, arterial atherosclerosis, and altered hemodynamics.

CONCLUSION

Most of the above mentioned vascular effects of stress were primarily reported in animal models. Further in-vivo standardization of pathological vascular indices and imaging modalities is warranted. Radiological quantification of these cerebrovascular changes is therefore essential for in depth understanding of the healthy and diseased cerebral arteries functions, identification and stratification of patients at risk of cardiovascular and neurological adverse events, enactment of preventive measures prior to the onset of systemic HTN, and the initiation of personalized medical management.

Ab initio investigation of ground and excited states of ScH molecule

The theoretical study of the electronic structure of Scandium Hydride ScH has been carried out using ab-initio methods. By employing (MRCI-SD/SA-CASSCF) and by using basis sets involving contribution of all electrons of both Scandium and Hydrogen atoms, 18 singlet and 15 triplet low-lying electronic states have been calculated below 28803 cm^-1. Potential energy curves have been plotted and the term values at equilibrium T_e, the vibrational constants ω_e and ω_eχ_e of ScH electronic states have been fitted. In addition, we calculated the permanent electric dipole moments for all these predicted states, the transition dipole moments TDMs within states at a range of internuclear distance around the equilibrium, the vibronic intensities FCF and the radiative lifetime. The calculated spectroscopic constants are in excellent consistency with the available experimental results and with other previous theoretical works. These calculations also predicted many excited states unobserved experimentally. The observed perturbations in D¹Π and C¹Σ⁺ have been investigated in this work.

Early assessment of lung function in coronavirus patients using invariant markers from chest X-rays images

The primary goal of this manuscript is to develop a computer assisted diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data and provides accurate, objective imaging markers to assist in the determination of patients with a higher risk of death and thus are more likely to require mechanical ventilation and/or more intensive clinical care.To obtain an accurate stochastic model that has the ability to detect the severity of lung infection, we develop a second-order Markov-Gibbs random field (MGRF) invariant under rigid transformation (translation or rotation of the image) as well as scale (i.e., pixel size). The parameters of the MGRF model are learned automatically, given a training set of X-ray images with affected lung regions labeled. An X-ray input to the system undergoes pre-processing to correct for non-uniformity of illumination and to delimit the boundary of the lung, using either a fully-automated segmentation routine or manual delineation provided by the radiologist, prior to the diagnosis. The steps of the proposed methodology are: (i) estimate the Gibbs energy at several different radii to describe the inhomogeneity in lung infection; (ii) compute the cumulative distribution function (CDF) as a new representation to describe the local inhomogeneity in the infected region of lung; and (iii) input the CDFs to a new neural network-based fusion system to determine whether the severity of lung infection is low or high. This approach is tested on 200 clinical X-rays from 200 COVID-19 positive patients, 100 of whom died and 100 who recovered using multiple training/testing processes including leave-one-subject-out (LOSO), tenfold, fourfold, and twofold cross-validation tests. The Gibbs energy for lung pathology was estimated at three concentric rings of increasing radii. The accuracy and Dice similarity coefficient (DSC) of the system steadily improved as the radius increased. The overall CAD system combined the estimated Gibbs energy information from all radii and achieved a sensitivity, specificity, accuracy, and DSC of 100%, 97% ± 3%, 98% ± 2%, and 98% ± 2%, respectively, by twofold cross validation. Alternative classification algorithms, including support vector machine, random forest, naive Bayes classifier, K-nearest neighbors, and decision trees all produced inferior results compared to the proposed neural network used in this CAD system. The experiments demonstrate the feasibility of the proposed system as a novel tool to objectively assess disease severity and predict mortality in COVID-19 patients. The proposed tool can assist physicians to determine which patients might require more intensive clinical care, such a mechanical respiratory support.

Long-distance running training system based on inertial sensor network

Long-distance running is an advantage of Chinese sports, but compared with the world level, there is still a big gap. Therefore, an advanced long-distance running training system is urgently needed to scientifically train our long-distance runners to change this situation. The purpose of this article is to study the long-distance running training system under inertial sensor network. According to the actual situation at home and abroad, a human gait analysis system based on inertial sensors is designed. Gait parameters are transformed into clinical medicine through related algorithms and software platforms. Experimental results show that although the step length calculated by the gait analysis system is different from the actual step length, the error value is small, kept below 3 cm, and the error percentage is less than 2%, which meets the accuracy requirements of gait analysis. This fully proves the feasibility of the zero-speed correction method in gait analysis.

A deep learning-based approach for automatic segmentation and quantification of the left ventricle from cardiac cine MR images

Cardiac MRI has been widely used for noninvasive assessment of cardiac anatomy and function as well as heart diagnosis. The estimation of physiological heart parameters for heart diagnosis essentially require accurate segmentation of the Left ventricle (LV) from cardiac MRI. Therefore, we propose a novel deep learning approach for the automated segmentation and quantification of the LV from cardiac cine MR images. We aim to achieve lower errors for the estimated heart parameters compared to the previous studies by proposing a novel deep learning segmentation method. Our framework starts by an accurate localization of the LV blood pool center-point using a fully convolutional neural network (FCN) architecture called FCN1. Then, a region of interest (ROI) that contains the LV is extracted from all heart sections. The extracted ROIs are used for the segmentation of LV cavity and myocardium via a novel FCN architecture called FCN2. The FCN2 network has several bottleneck layers and uses less memory footprint than conventional architectures such as U-net. Furthermore, a new loss function called radial loss that minimizes the distance between the predicted and true contours of the LV is introduced into our model. Following myocardial segmentation, functional and mass parameters of the LV are estimated. Automated Cardiac Diagnosis Challenge (ACDC-2017) dataset was used to validate our framework, which gave better segmentation, accurate estimation of cardiac parameters, and produced less error compared to other methods applied on the same dataset. Furthermore, we showed that our segmentation approach generalizes well across different datasets by testing its performance on a locally acquired dataset. To sum up, we propose a deep learning approach that can be translated into a clinical tool for heart diagnosis.

A novel computer-aided diagnosis system for the early detection of hypertension based on cerebrovascular alterations

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3-D CNN segmentation succeeded in delineating cerebrovasculature accurately.

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Segmentation approach is automatic and applicable on healthy/pathological vessels.

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Blood flow variability challenge was addressed by processing MRA scans locally.

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Proposed vascular features were efficient to quantify cerebral changes.

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Proposed CAD system could help clinicians predict hypertension before its onset.

Hypertension is a leading cause of mortality in the USA. While simple tools such as the sphygmomanometer are widely used to diagnose hypertension, they could not predict the disease before its onset. Clinical studies suggest that alterations in the structure of human brains’ cerebrovasculature start to develop years before the onset of hypertension. In this research, we present a novel computer-aided diagnosis (CAD) system for the early detection of hypertension. The proposed CAD system analyzes magnetic resonance angiography (MRA) data of human brains to detect and track the cerebral vascular alterations and this is achieved using the following steps: i) MRA data are preprocessed to eliminate noise effects, correct the bias field effect, reduce the contrast inhomogeneity using the generalized Gauss-Markov random field (GGMRF) model, and normalize the MRA data, ii) the cerebral vascular tree of each MRA volume is segmented using a 3-D convolutional neural network (3D-CNN), iii) cerebral features in terms of diameters and tortuosity of blood vessels are estimated and used to construct feature vectors, iv) feature vectors are then used to train and test various artificial neural networks to classify data into two classes; normal and hypertensive. A balanced data set of 66 subjects were used to test the CAD system. Experimental results reported a classification accuracy of 90.9% which supports the efficacy of the CAD system components to accurately model and discriminate between normal and hypertensive subjects. Clinicians would benefit from the proposed CAD system to detect and track cerebral vascular alterations over time for people with high potential of developing hypertension and to prepare appropriate treatment plans to mitigate adverse events.

A Deep Learning-Based Approach for the Detection and Localization of Prostate Cancer in T2 Magnetic Resonance Images

We address the problem of prostate lesion detection, localization, and segmentation in T2W magnetic resonance (MR) images. We train a deep convolutional encoder-decoder architecture to simultaneously segment the prostate, its anatomical structure, and the malignant lesions. To incorporate the 3D contextual spatial information provided by the MRI series, we propose a novel 3D sliding window approach, which preserves the 2D domain complexity while exploiting 3D information. Experiments on data from 19 patients provided for the public by the Initiative for Collaborative Computer Vision Benchmarking (I2CVB) show that our approach outperforms traditional pattern recognition and machine learning approaches by a significant margin. Particularly, for the task of cancer detection and localization, the system achieves an average AUC of 0.995, an accuracy of 0.894, and a recall of 0.928. The proposed mono-modal deep learning-based system performs comparably to other multi-modal MR-based systems. It could improve the performance of a radiologist in prostate cancer diagnosis and treatment planning.

A Generalized Deep Learning-Based Diagnostic System for Early Diagnosis of Various Types of Pulmonary Nodules

A novel framework for the classification of lung nodules using computed tomography scans is proposed in this article. To get an accurate diagnosis of the detected lung nodules, the proposed framework integrates the following 2 groups of features: (1) appearance features modeled using the higher order Markov Gibbs random field model that has the ability to describe the spatial inhomogeneities inside the lung nodule and (2) geometric features that describe the shape geometry of the lung nodules. The novelty of this article is to accurately model the appearance of the detected lung nodules using a new developed seventh-order Markov Gibbs random field model that has the ability to model the existing spatial inhomogeneities for both small and large detected lung nodules, in addition to the integration with the extracted geometric features. Finally, a deep autoencoder classifier is fed by the above 2 feature groups to distinguish between the malignant and benign nodules. To evaluate the proposed framework, we used the publicly available data from the Lung Image Database Consortium. We used a total of 727 nodules that were collected from 467 patients. The proposed system demonstrates the promise to be a valuable tool for the detection of lung cancer evidenced by achieving a nodule classification accuracy of 91.20%.

Computer-Aided Diagnostic System for Early Detection of Acute Renal Transplant Rejection Using Diffusion-Weighted MRI

OBJECTIVE

Early diagnosis of acute renal transplant rejection (ARTR) is critical for accurate treatment. Although the current gold standard, diagnostic technique is renal biopsy, it is not preferred due to its invasiveness, long recovery time (1-2 weeks), and potential for complications, e.g., bleeding and/or infection.

METHODS

This paper presents a computer-aided diagnostic (CAD) system for early ARTR detection using (3D + b-value) diffusion-weighted (DW) magnetic resonance imaging (MRI) data. The CAD process starts from kidney tissue segmentation with an evolving geometric (level-set-based) deformable model. The evolution is guided by a voxel-wise stochastic speed function, which follows from a joint kidney-background Markov-Gibbs random field model accounting for an adaptive kidney shape prior and on-going kidney-background visual appearances. A B-spline-based three-dimensional data alignment is employed to handle local deviations due to breathing and heart beating. Then, empirical cumulative distribution functions of apparent diffusion coefficients of the segmented DW-MRI at different b-values are collected as discriminatory transplant status features. Finally, a deep-learning-based classifier with stacked nonnegative constrained autoencoders is employed to distinguish between rejected and nonrejected renal transplants.

RESULTS

In our initial "leave-one-subject-out" experiment on 100 subjects, [Formula: see text] of the subjects were correctly classified. The subsequent four-fold and ten-fold cross-validations gave the average accuracy of [Formula: see text] and [Formula: see text], respectively.

CONCLUSION

These results demonstrate the promise of this new CAD system to reliably diagnose renal transplant rejection.

SIGNIFICANCE

The technology presented here can significantly impact the quality of care of renal transplant patients since it has the potential to replace the gold standard in kidney diagnosis, biopsy.

A new hybrid watermarking algorithm for MRI medical images using DWT and hash functions

This paper deals with a blind hybrid digital watermarking algorithm for the copyright protection and authentication of magnetic resonance tomography images. Medical image watermarking requires extreme care when embedding watermark information in the medical images, to protect the image quality from being violated and to avoid the wrong diagnosis that might occur. The proposed algorithm contains robust watermark for the ownership protection and fragile watermarks for checking the authenticity. In the proposed algorithm, the medical image is divided into two regions, called the Region of Interest and Region of Non-Interest using histogram technique and the watermarks are embedded in both wavelet domain and spatial domain in the Region of Non-Interest Area. The proposed hybrid watermarking technique was successfully tested on a variety of MRI medical images and offerd high peak signal to noise ratio and similarity structure index measure values.

[Aortofemoral vascular graft infections and their prevention]

Vascular prosthesis infections are potentially severe adverse events following vascular reconstruction. They are often associated with a high morbidity and mortality, especially in the aortofemoral region. The present article outlines the diagnosis, prevention and treatment of vascular graft infections in a clinical setting. The clinical presentation, inflammatory markers, microbiological work-up and imaging studies can contribute to diagnosing a prosthesis infection. Regarding the bacterial spectrum involved in the etiology of prosthesis infections, single organism infections (monoinfections) have become less significant over the past years, whereas infections with multiple organisms now constitute the most abundant microbiological constellation. Also, infections with resistant bacterial strains have been increasing in number over the past years and deserve special consideration. It remains unclear whether both aspects are due to a true epidemiological change or are the result of advanced molecular microbiological diagnostic methods. While during the past decades perioperative antibiotic prophylaxis was regarded as the most important measure for preventing prosthesis infections in vascular surgery, other primary preventive hygiene strategies have been increasingly explored and grouped together in the sense of preventive bundles. In most cases of deep postoperative infections involving a prosthetic device in the aortofemoral region, explantation of the prosthesis will be required. In situ and extra-anatomical reconstructions are often performed in such cases and the decision process to develop an optimal treatment plan must consider several individual factors. In select patients, palliative preservation of the prosthesis despite surrounding infection (i.e. graft salvage) and best conservative management in combination with local surgical measures, such as incision and drainage and vacuum therapy, deserve consideration as a treatment option for patients with a high surgical risk.

Theoretical investigation of the lowest-lying electronic structure of LuI molecules

CASSCF/MRCI calculations using Effective Core Potential (ECP) basis sets for both Lu and I atoms, have been performed for the first 22 electronic states in the representation (2s+1)Λ((±)) for the LuI molecule. This investigation included the corresponding 43 molecular states in the representation Ω((±)) when taking the spin-orbit coupling (SOC) in consideration. Calculated potential energy curves (PECs) have been displayed. Spectroscopic constants T(e), ω(e), ω(e)χ(e), B(e) and the internuclear distance R(e) have been calculated for the ground state and for the low-lying electronic states situated below 40,410 cm(-1) and for their corresponding components with SOC. The transition dipolar moments between states have been given at the minimum position R(e)=2.75 Å of the ground state X(1)Σ(+). The calculated set of singlet and triplet states provides a theoretical prediction for more than 19 yet unobserved electronic states.

Ab initio study of the lowest-lying electronic states of LuCl molecules

By using the CASSCF/MRCI methods, the theoretical electronic structure of the LuCl molecule has been investigated. These methods have been performed for 20 singlet and triplet electronic states in the representation (2s+1)Λ((±)). Calculated potential energy curves (PECs) are also displayed. Spectroscopic constants including the harmonic vibrational wavenumber ω(e) (cm(-1)), the relative electronic energy T(e) (cm(-1)) referred to the ground state, and the equilibrium internuclear distance R(e) (Å) have been predicted for all of the singlet and triplet electronic states situated below 43,000 cm(-1). Spin-orbit effects have also been taken into consideration and calculated for the lowest-lying electronic states in the representation Ω((±)).

The B1Pi --> X1Sigma+ System of Scandium Monoiodide

Rotational analyses have been carried out on eight thermically excited emission bands of the infrared B1Pi --> X1Sigma+ system of scandium monoiodide between 4000 to 5000 cm-1, recorded by Fourier transform spectrometry. Rotational constants and energies are obtained for X1Sigma+ (v = 0, 1, 2) and B1Pi (v = 0-4) levels from which equilibrium constants of the states are derived. Perturbational effects are observed in the bands with v" = 1 that are interpreted as consequences of the avoided crossing of X1Sigma+ (v = 1) and a3Delta1 (v = 0) at J = 70. A treatment of the perturbation is made using an effective 2 x 2 matrix representation of the rotational energies of the two levels. Copyright 1998 Academic Press.

Effect of volume history on changes in DLcoSB-3EQ with lung volume in normal subjects

The purpose of this study was to determine the relationship between the three-equation diffusing capacity for carbon monoxide (DLcoSB-3EQ) and lung volume and to determine how this relationship was altered when maneuvers were immediately preceded by a deep breath. DLcoSB-3EQ maneuvers were performed in nine healthy subjects either immediately after a deep breath or after tidal breathing for 10 min. The maneuvers consisted of slow inhalation of test gas from functional residual capacity to 25, 50, 75, or 100% of the inspiratory capacity and, without breath holding, slow exhalation to residual volume. After either a deep breath or tidal breathing, we found that DLcoSB-3EQ decreased nonlinearly with decreasing lung volume. At all lung volumes, DLcoSB-3EQ was significantly greater when measured after a deep breath than after tidal breathing. This effect increased as lung volume decreased, so that the greatest difference between DLcoSB-3EQ after a deep breath and that after tidal breathing occurred at the lowest lung volume. We conclude that a deep breath or spontaneous sigh has a role in reestablishing the pathway for gas exchange during tidal breathing.